From ac099f37a7fd1b7f6838a12fa6d24a35528efa57 Mon Sep 17 00:00:00 2001 From: Shinya Uryu Date: Wed, 10 Jul 2019 07:56:06 +0900 Subject: [PATCH] =?UTF-8?q?evalation=E3=82=92=E8=AA=BF=E6=95=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- _build/01/eda.md | 559 +++++++++++++----- _build/01/tidy-data.md | 543 +++++++++++++---- _build/01/tidymodels-workflow.md | 140 +++-- _build/02/categorical.md | 482 +++++++++++---- _build/02/date-and-time.md | 243 +++++--- _build/02/numeric.md | 404 +++++++------ _build/02/spatial-data.md | 162 ++--- _build/02/text.md | 416 +++++++++++-- .../images/beer_holiday_effect_comperad-1.png | Bin 0 -> 414368 bytes _build/images/beer_ts_window-1.png | Bin 0 -> 399965 bytes _build/images/lp_categorize_distance-1.png | Bin 0 -> 75780 bytes _build/images/lp_correlation_ggally-1.png | Bin 0 -> 222982 bytes _build/images/lp_interactions-1.png | Bin 0 -> 120459 bytes ...tance_from_station_posted_land_price-1.png | Bin 0 -> 95228 bytes ...m_station_posted_land_price_logscale-1.png | Bin 0 -> 284880 bytes _build/images/pref08_categorical_count-1.png | Bin 2353292 -> 914861 bytes _build/images/scale_histogram_comared-1.png | Bin 0 -> 80002 bytes _build/images/step_hyperbolic-1.png | Bin 0 -> 283176 bytes _build/images/unnamed-chunk-15-1.png | Bin 0 -> 314784 bytes content/01/eda.Rmd | 23 +- content/01/tidy-data.Rmd | 56 +- content/01/tidymodels-workflow.Rmd | 1 + content/02/categorical.Rmd | 13 +- content/02/date-and-time.Rmd | 38 +- content/02/numeric.Rmd | 23 +- content/02/spatial-data.Rmd | 9 +- content/02/text.Rmd | 17 +- 27 files changed, 2193 insertions(+), 936 deletions(-) create mode 100644 _build/images/beer_holiday_effect_comperad-1.png create mode 100644 _build/images/beer_ts_window-1.png create mode 100644 _build/images/lp_categorize_distance-1.png create mode 100644 _build/images/lp_correlation_ggally-1.png create mode 100644 _build/images/lp_interactions-1.png create mode 100644 _build/images/lp_scatter_distance_from_station_posted_land_price-1.png create mode 100644 _build/images/lp_scatter_distance_from_station_posted_land_price_logscale-1.png create mode 100644 _build/images/scale_histogram_comared-1.png create mode 100644 _build/images/step_hyperbolic-1.png create mode 100644 _build/images/unnamed-chunk-15-1.png diff --git a/_build/01/eda.md b/_build/01/eda.md index 40c13fb..50f192d 100644 --- a/_build/01/eda.md +++ b/_build/01/eda.md @@ -9,20 +9,16 @@ prev_page: next_page: url: /01/tidymodels-workflow title: 'モデルの構築から評価まで' +output: github_document + comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" --- - - -{:.input_area} -``` -source(here::here("R/setup.R")) -``` - - # 探索的データ分析 -データを料理する前に、どのようなデータが与えられているのか確認することが大切です。この段階を踏むことで、データに対する理解が深まり、より良いモデルの構築に繋がる可能性もあります。こうした一連の作業は探索的データ分析 (Exploratory Data Analysis: EDA)と呼ばれます。この作業には、データの集計、要約、可視化が含まれます。 +データを料理する前に、どのようなデータが与えられているのか確認することが大切です。この段階を踏むことで、データに対する理解が深まり、より良いモデルの構築に繋がる可能性もあります。こうした一連の作業は探索的データ分析 +(Exploratory Data Analysis: +EDA)と呼ばれます。この作業には、データの集計、要約、可視化が含まれます。 EDAがデータ分析の作業において早期段階で行われるのは、データの異常(思い込みとの比較を含めて)や特徴を把握するためです。これらは分析全体のアプローチや良い出発点を見つけるために有効です。出発点と表現したのは、モデルの構築や特徴量の生成によって改めてデータを見つめ直す作業が発生するためです。そのため必ずしも徹底的である必要はありません。 @@ -36,75 +32,187 @@ EDAがデータ分析の作業において早期段階で行われるのは、 データを手に入れたら、分析作業へ取り掛かる前にまずはデータを眺めてみることにしましょう。眺める、と言ってもデータの値を1つずつ見ていくわけではありません。これから扱うデータにはどのような値が含まれているのか、データ型が処理されているか、また全体の大きさはどれくらいなのか欠損はどれだけあるかと言った情報を俯瞰的に整理していきます。それには以下のような項目があります。 -- サイズ(列数、行数) -- 各列のデータ型 -- 完全データ、欠損データ + - サイズ(列数、行数) + - 各列のデータ型 + - 完全データ、欠損データ ### データの大きさ データフレームは行と列からなります。いくつかの関数を使って読み込んだデータの大きさを調べてみましょう。 - - {:.input_area} -``` +```R dim(df_lp_kanto) ``` + ## [1] 8476 45 `dim()`をデータフレームに適用すると、そのサイズを数値ベクトルで返します。最初の要素が行数、2番目の数字が列数を示します。これは次の`ncol()`、`nrow()`により個別に求めることができます。 +{:.input_area} +```R +nrow(df_lp_kanto) +``` - + ## [1] 8476 {:.input_area} -``` -nrow(df_lp_kanto) +```R ncol(df_lp_kanto) ``` + ## [1] 45 ### データの一部を表示 -データフレームの一部を表示して、列名と値の確認をしてみましょう。`head()`をデータフレームに対して実行すると先頭の数行を表示します。また`tail()` でデータフレームの最後の行を表示できます。いずれの関数も引数`n = ` に実数を与えることで表示される行数を制御可能です。 - - +データフレームの一部を表示して、列名と値の確認をしてみましょう。`head()`をデータフレームに対して実行すると先頭の数行を表示します。また`tail()` +でデータフレームの最後の行を表示できます。いずれの関数も引数`n =` に実数を与えることで表示される行数を制御可能です。 {:.input_area} -``` +```R head(df_lp_kanto) +``` + ## # A tibble: 6 x 45 + ## .row_id .prefecture administrative_… posted_land_pri… name_of_nearest… + ## + ## 1 13534 群馬県 10425 4150 万座・鹿沢口 + ## 2 13604 群馬県 10426 39000 長野原草津口 + ## 3 13605 群馬県 10426 56800 長野原草津口 + ## 4 13613 群馬県 10424 2700 羽根尾 + ## 5 13620 群馬県 10424 12100 羽根尾 + ## 6 13621 群馬県 10426 29800 長野原草津口 + ## # … with 40 more variables: distance_from_station , acreage , + ## # current_use , usage_description , building_structure , + ## # attribute_change_supplied_facility , water_facility , + ## # gas_facility , sewage_facility , + ## # proximity_with_transportation_facility , depth_ratio , + ## # number_of_floors , number_of_basement_floors , + ## # use_district , building_coverage , configuration , + ## # surrounding_present_usage , fire_area , + ## # urban_planning_area , forest_law , parks_law , + ## # attribute_change_floor_area_ratio , frontage_ratio , + ## # floor_area_ratio , attribute_change_selected_land_status , + ## # attribute_change_address , attribute_change_acreage , + ## # attribute_change_current_use , + ## # attribute_change_building_structure , + ## # attribute_change_distance_from_station , + ## # attribute_change_use_district , attribute_change_fire_area , + ## # attribute_change_urban_planning_area , + ## # attribute_change_forest_law , attribute_change_parks_law , + ## # attribute_change_building_coverage , + ## # common_surveyed_position , .longitude , .latitude , + ## # night_population + +{:.input_area} +```R tail(df_lp_kanto, n = 3) ``` + ## # A tibble: 3 x 45 + ## .row_id .prefecture administrative_… posted_land_pri… name_of_nearest… + ## + ## 1 23818 茨城県 08232 15600 銚子 + ## 2 23839 千葉県 12202 41200 銚子 + ## 3 23851 千葉県 12202 31500 銚子 + ## # … with 40 more variables: distance_from_station , acreage , + ## # current_use , usage_description , building_structure , + ## # attribute_change_supplied_facility , water_facility , + ## # gas_facility , sewage_facility , + ## # proximity_with_transportation_facility , depth_ratio , + ## # number_of_floors , number_of_basement_floors , + ## # use_district , building_coverage , configuration , + ## # surrounding_present_usage , fire_area , + ## # urban_planning_area , forest_law , parks_law , + ## # attribute_change_floor_area_ratio , frontage_ratio , + ## # floor_area_ratio , attribute_change_selected_land_status , + ## # attribute_change_address , attribute_change_acreage , + ## # attribute_change_current_use , + ## # attribute_change_building_structure , + ## # attribute_change_distance_from_station , + ## # attribute_change_use_district , attribute_change_fire_area , + ## # attribute_change_urban_planning_area , + ## # attribute_change_forest_law , attribute_change_parks_law , + ## # attribute_change_building_coverage , + ## # common_surveyed_position , .longitude , .latitude , + ## # night_population ### 各列の情報 データ型や件数、欠損の状況を調べます。 - - {:.input_area} -``` +```R glimpse(df_lp_kanto) ``` + ## Observations: 8,476 + ## Variables: 45 + ## $ .row_id 13534, 13604, 13605, 136… + ## $ .prefecture "群馬県", "群馬県", "群馬県", "群馬… + ## $ administrative_area_code "10425", "10426", "10426… + ## $ posted_land_price 4150, 39000, 56800, 2700… + ## $ name_of_nearest_station "万座・鹿沢口", "長野原草津口", "長野原… + ## $ distance_from_station 8700, 13000, 13000, 5500… + ## $ acreage 317, 166, 226, 274, 357,… + ## $ current_use "住宅,その他", "住宅", "店舗", "住… + ## $ usage_description "別荘", NA, NA, "別荘", NA, … + ## $ building_structure "W1", "W2", "W1", "W2", … + ## $ attribute_change_supplied_facility FALSE, FALSE, FALSE, FAL… + ## $ water_facility TRUE, TRUE, TRUE, TRUE, … + ## $ gas_facility FALSE, FALSE, FALSE, FAL… + ## $ sewage_facility FALSE, TRUE, TRUE, TRUE,… + ## $ proximity_with_transportation_facility NA, NA, NA, NA, NA, NA, … + ## $ depth_ratio 1.0, 1.0, 2.0, 2.0, 1.5,… + ## $ number_of_floors 1, 2, 1, 2, 2, 2, 2, 1, … + ## $ number_of_basement_floors 0, 0, 0, 0, 0, 0, 0, 0, … + ## $ use_district NA, "2住居", "近商", NA, NA,… + ## $ building_coverage 0, 60, 80, 70, 70, 60, 7… + ## $ configuration NA, NA, "台形", NA, NA, NA… + ## $ surrounding_present_usage "低層別荘が建ち並ぶ環境の良い別荘地域", "一… + ## $ fire_area NA, NA, NA, NA, NA, NA, … + ## $ urban_planning_area "都計外", "非線引", "非線引", "非線… + ## $ forest_law NA, NA, NA, NA, NA, NA, … + ## $ parks_law NA, NA, NA, NA, NA, NA, … + ## $ attribute_change_floor_area_ratio FALSE, FALSE, FALSE, FAL… + ## $ frontage_ratio 1.2, 1.2, 1.0, 1.0, 1.0,… + ## $ floor_area_ratio 0, 200, 300, 400, 400, 2… + ## $ attribute_change_selected_land_status 1, 1, 1, 1, 1, 1, 1, 1, … + ## $ attribute_change_address FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_acreage FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_current_use FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_building_structure FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_distance_from_station FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_use_district FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_fire_area FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_urban_planning_area FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_forest_law FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_parks_law FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_building_coverage FALSE, FALSE, FALSE, FAL… + ## $ common_surveyed_position FALSE, FALSE, FALSE, FAL… + ## $ .longitude 138.5383, 138.5921, 138.… + ## $ .latitude 36.46920, 36.61913, 36.6… + ## $ night_population 9780, 6518, 6518, 5536, … この地価公示データには45の列(変数)があります。 -- データの型 (`character`, `numeric`, `logical`, `factor`) -- 欠損数、ユニーク数 -- 文字データの長さの幅 -- カテゴリデータの偏り、水準、順序 -- 数値データの要約統計量、分布(ヒストグラム) + - データの型 (`character`, `numeric`, `logical`, + `factor`) + + - 欠損数、ユニーク数 -- `configuration`や`fire_area`は欠損を多く含んでいることがわかります。`building_structure`にもわずかですが欠損データがあります + - 文字データの長さの幅 + - カテゴリデータの偏り、水準、順序 + - 数値データの要約統計量、分布(ヒストグラム) + - `configuration`や`fire_area`は欠損を多く含んでいることがわかります。`building_structure`にもわずかですが欠損データがあります + + {:.input_area} -``` +```R # 同一のデータで重複があるもの df_lp_kanto %>% tidyr::separate_rows("current_use", sep = ",") %>% @@ -115,25 +223,141 @@ df_lp_kanto %>% ensure(. == 1986L) ``` + ## [1] 1986 明確な答えがあるわけではありませんが、以下の情報はデータ全体、各変数について把握しておくと良いでしょう。またデータの偏りや出現傾向、分布のパターンは可視化を行い確認するのが効率的です。 ## 要約統計量の算出 - - {:.input_area} -``` +```R summary(df_lp_kanto) ``` + ## .row_id .prefecture administrative_area_code + ## Min. :13534 Length:8476 Length:8476 + ## 1st Qu.:16404 Class :character Class :character + ## Median :18532 Mode :character Mode :character + ## Mean :18584 + ## 3rd Qu.:20708 + ## Max. :23851 + ## posted_land_price name_of_nearest_station distance_from_station + ## Min. : 613 Length:8476 Min. : 0 + ## 1st Qu.: 59900 Class :character 1st Qu.: 500 + ## Median : 155000 Mode :character Median : 1000 + ## Mean : 415432 Mean : 1566 + ## 3rd Qu.: 308000 3rd Qu.: 1800 + ## Max. :55500000 Max. :24000 + ## acreage current_use usage_description building_structure + ## Min. : 46 Length:8476 Length:8476 Length:8476 + ## 1st Qu.: 140 Class :character Class :character Class :character + ## Median : 181 Mode :character Mode :character Mode :character + ## Mean : 751 + ## 3rd Qu.: 272 + ## Max. :413215 + ## attribute_change_supplied_facility water_facility gas_facility + ## Mode :logical Mode :logical Mode :logical + ## FALSE:8429 FALSE:49 FALSE:1860 + ## TRUE :47 TRUE :8427 TRUE :6616 + ## + ## + ## + ## sewage_facility proximity_with_transportation_facility depth_ratio + ## Mode :logical Length:8476 Min. :0.000 + ## FALSE:701 Class :character 1st Qu.:1.000 + ## TRUE :7775 Mode :character Median :1.200 + ## Mean :1.459 + ## 3rd Qu.:1.500 + ## Max. :6.500 + ## number_of_floors number_of_basement_floors use_district + ## Min. : 0.000 Min. :0.00000 Length:8476 + ## 1st Qu.: 2.000 1st Qu.:0.00000 Class :character + ## Median : 2.000 Median :0.00000 Mode :character + ## Mean : 2.718 Mean :0.07574 + ## 3rd Qu.: 2.000 3rd Qu.:0.00000 + ## Max. :52.000 Max. :6.00000 + ## building_coverage configuration surrounding_present_usage + ## Min. : 0.00 Length:8476 Length:8476 + ## 1st Qu.:50.00 Class :character Class :character + ## Median :60.00 Mode :character Mode :character + ## Mean :60.17 + ## 3rd Qu.:60.00 + ## Max. :80.00 + ## fire_area urban_planning_area forest_law + ## Length:8476 Length:8476 Length:8476 + ## Class :character Class :character Class :character + ## Mode :character Mode :character Mode :character + ## + ## + ## + ## parks_law attribute_change_floor_area_ratio frontage_ratio + ## Length:8476 Mode :logical Min. :0.000 + ## Class :character FALSE:8474 1st Qu.:1.000 + ## Mode :character TRUE :2 Median :1.000 + ## Mean :1.066 + ## 3rd Qu.:1.000 + ## Max. :5.500 + ## floor_area_ratio attribute_change_selected_land_status + ## Min. : 0.0 Min. :1.000 + ## 1st Qu.: 100.0 1st Qu.:1.000 + ## Median : 200.0 Median :1.000 + ## Mean : 216.6 Mean :1.027 + ## 3rd Qu.: 200.0 3rd Qu.:1.000 + ## Max. :1300.0 Max. :4.000 + ## attribute_change_address attribute_change_acreage + ## Mode :logical Mode :logical + ## FALSE:8411 FALSE:8450 + ## TRUE :65 TRUE :26 + ## + ## + ## + ## attribute_change_current_use attribute_change_building_structure + ## Mode :logical Mode :logical + ## FALSE:8402 FALSE:8406 + ## TRUE :74 TRUE :70 + ## + ## + ## + ## attribute_change_distance_from_station attribute_change_use_district + ## Mode :logical Mode :logical + ## FALSE:8431 FALSE:8475 + ## TRUE :45 TRUE :1 + ## + ## + ## + ## attribute_change_fire_area attribute_change_urban_planning_area + ## Mode :logical Mode :logical + ## FALSE:8417 FALSE:8475 + ## TRUE :59 TRUE :1 + ## + ## + ## + ## attribute_change_forest_law attribute_change_parks_law + ## Mode :logical Mode :logical + ## FALSE:8476 FALSE:8476 + ## + ## + ## + ## + ## attribute_change_building_coverage common_surveyed_position + ## Mode :logical Mode :logical + ## FALSE:8474 FALSE:7870 + ## TRUE :2 TRUE :606 + ## + ## + ## + ## .longitude .latitude night_population + ## Min. :138.5 Min. :34.98 Min. : 3214 + ## 1st Qu.:139.5 1st Qu.:35.59 1st Qu.:118919 + ## Median :139.7 Median :35.72 Median :200012 + ## Mean :139.7 Mean :35.79 Mean :259450 + ## 3rd Qu.:139.9 3rd Qu.:35.92 3rd Qu.:341076 + ## Max. :140.8 Max. :37.02 Max. :903346 Rの組み込み関数として用意されている `summary()` を利用して、データフレームに含まれる列の要約統計量を得ることができます。 - - {:.input_area} -``` +```R # 論理値型で標準偏差0の列を特定 constant cols df_lp_kanto %>% select(-starts_with(".")) %>% @@ -143,21 +367,103 @@ df_lp_kanto %>% tidyr::gather() ``` + ## # A tibble: 17 x 2 + ## key value + ## + ## 1 attribute_change_supplied_facility 0.0743 + ## 2 water_facility 0.0758 + ## 3 gas_facility 0.414 + ## 4 sewage_facility 0.275 + ## 5 attribute_change_floor_area_ratio 0.0154 + ## 6 attribute_change_address 0.0872 + ## 7 attribute_change_acreage 0.0553 + ## 8 attribute_change_current_use 0.0930 + ## 9 attribute_change_building_structure 0.0905 + ## 10 attribute_change_distance_from_station 0.0727 + ## 11 attribute_change_use_district 0.0109 + ## 12 attribute_change_fire_area 0.0831 + ## 13 attribute_change_urban_planning_area 0.0109 + ## 14 attribute_change_forest_law 0 + ## 15 attribute_change_parks_law 0 + ## 16 attribute_change_building_coverage 0.0154 + ## 17 common_surveyed_position 0.258 ここでは `skimr` で行う例を示します。 - - {:.input_area} -``` +```R # skimr::skim(df_lp) # Rコンソールでの実行は skim() で構いません df_lp_kanto %>% select(-starts_with(".")) %>% skimr::skim_to_list() ``` - -論理型データのうち、`attribute_change_forest_law` と `attribute_change_parks_law` は FALSE のみが出現していることがわかります。 + ## $character + ## # A tibble: 13 x 8 + ## variable missing complete n min max empty n_unique + ## * + ## 1 administrative_area_c… 0 8476 8476 5 5 0 329 + ## 2 building_structure 220 8256 8476 2 8 0 130 + ## 3 configuration 7454 1022 8476 2 3 0 2 + ## 4 current_use 0 8476 8476 2 14 0 70 + ## 5 fire_area 4859 3617 8476 2 2 0 2 + ## 6 forest_law 8450 26 8476 3 3 0 1 + ## 7 name_of_nearest_stati… 0 8476 8476 1 14 0 1481 + ## 8 parks_law 8465 11 8476 7 7 0 2 + ## 9 proximity_with_transp… 8308 168 8476 2 6 0 3 + ## 10 surrounding_present_u… 0 8476 8476 15 22 0 4397 + ## 11 urban_planning_area 0 8476 8476 2 3 0 4 + ## 12 usage_description 7552 924 8476 1 8 0 40 + ## 13 use_district 515 7961 8476 2 3 0 12 + ## + ## $integer + ## # A tibble: 5 x 12 + ## variable missing complete n mean sd p0 p25 p50 p75 p100 + ## * + ## 1 acreage 0 8476 8476 " … " … 46 140 181 272 "413… + ## 2 distanc… 0 8476 8476 " 1… " … 0 500 1000 1800 " 24… + ## 3 number_… 0 8476 8476 " … " … 0 0 0 0 " … + ## 4 number_… 0 8476 8476 " … " … 0 2 2 2 " … + ## 5 posted_… 0 8476 8476 "415… 1766… 613 59900 1550… 3080… " … + ## # … with 1 more variable: hist + ## + ## $logical + ## # A tibble: 17 x 6 + ## variable missing complete n mean count + ## * + ## 1 attribute_change_acreage 0 8476 8476 "0.003… FAL: 8450, TRU:… + ## 2 attribute_change_address 0 8476 8476 "0.007… FAL: 8411, TRU:… + ## 3 attribute_change_buildi… 0 8476 8476 0.00024 FAL: 8474, TRU:… + ## 4 attribute_change_buildi… 0 8476 8476 "0.008… FAL: 8406, TRU:… + ## 5 attribute_change_curren… 0 8476 8476 "0.008… FAL: 8402, TRU:… + ## 6 attribute_change_distan… 0 8476 8476 "0.005… FAL: 8431, TRU:… + ## 7 attribute_change_fire_a… 0 8476 8476 "0.007… FAL: 8417, TRU:… + ## 8 attribute_change_floor_… 0 8476 8476 0.00024 FAL: 8474, TRU:… + ## 9 attribute_change_forest… 0 8476 8476 "0 … FAL: 8476, NA: 0 + ## 10 attribute_change_parks_… 0 8476 8476 "0 … FAL: 8476, NA: 0 + ## 11 attribute_change_suppli… 0 8476 8476 "0.005… FAL: 8429, TRU:… + ## 12 attribute_change_urban_… 0 8476 8476 0.00012 FAL: 8475, TRU:… + ## 13 attribute_change_use_di… 0 8476 8476 0.00012 FAL: 8475, TRU:… + ## 14 common_surveyed_position 0 8476 8476 "0.071… FAL: 7870, TRU:… + ## 15 gas_facility 0 8476 8476 "0.78 … TRU: 6616, FAL:… + ## 16 sewage_facility 0 8476 8476 "0.92 … TRU: 7775, FAL:… + ## 17 water_facility 0 8476 8476 "0.99 … TRU: 8427, FAL:… + ## + ## $numeric + ## # A tibble: 6 x 12 + ## variable missing complete n mean sd p0 p25 p50 p75 p100 + ## * + ## 1 attribu… 0 8476 8476 " … " … 1 1 " … " … " … + ## 2 buildin… 0 8476 8476 " … " … 0 50 " … " … " … + ## 3 depth_r… 0 8476 8476 " … " … 0 1 " … " … " … + ## 4 floor_a… 0 8476 8476 " … " 1… 0 100 " 2… " … " 13… + ## 5 frontag… 0 8476 8476 " … " … 0 1 " … " … " … + ## 6 night_p… 0 8476 8476 2594… "2e+… 3214 1189… "2e+… "341… "9e+… + ## # … with 1 more variable: hist + +論理型データのうち、`attribute_change_forest_law` と `attribute_change_parks_law` は +FALSE +のみが出現していることがわかります。 ## 探索的データ分析 @@ -171,23 +477,18 @@ df_lp_kanto %>% ## 数値データ - - {:.input_area} -``` +```R df_is_num <- df_lp_kanto %>% select(-starts_with(".")) %>% select_if(is_numeric) ``` - ## カテゴリデータ - - {:.input_area} -``` +```R df_is_cat <- df_lp_kanto %>% select(-starts_with(".")) %>% @@ -195,22 +496,31 @@ df_is_cat <- verify(ncol(.) == 13) ``` - - - {:.input_area} -``` +```R df_is_cat %>% count(name_of_nearest_station, sort = TRUE) ``` + ## # A tibble: 1,481 x 2 + ## name_of_nearest_station n + ## + ## 1 本厚木 59 + ## 2 水戸 56 + ## 3 宇都宮 55 + ## 4 平塚 51 + ## 5 八王子 48 + ## 6 柏 41 + ## 7 川崎 40 + ## 8 木更津 36 + ## 9 千葉 35 + ## 10 前橋 34 + ## # … with 1,471 more rows ## 論理値 - - {:.input_area} -``` +```R df_is_log <- df_lp_kanto %>% select(-starts_with(".")) %>% @@ -218,11 +528,8 @@ df_is_log <- verify(ncol(.) == 17) ``` - - - {:.input_area} -``` +```R gg_count_var <- function(data, var) { var <- rlang::enquo(var) var_label <- rlang::quo_name(var) @@ -235,23 +542,17 @@ gg_count_var <- function(data, var) { } ``` - - - {:.input_area} -``` +```R df_is_cat %>% filter(stringr::str_detect(administrative_area_code, "^08")) %>% gg_count_var(name_of_nearest_station) ``` - ![](../images/pref08_name_of_nearest_station-1.png) - - {:.input_area} -``` +```R purrr::map( rlang::syms(str_subset(names(df_is_cat), "name_of_nearest_station", negate = TRUE)), ~ gg_count_var(df_is_cat %>% @@ -260,22 +561,17 @@ purrr::map( plot_grid(plotlist = ., ncol = 2) ``` - ![](../images/pref08_categorical_count-1.png) 何枚かの図は文字が潰れてしまいましたが、いくつかのカテゴリデータについて掴めたことがあります。 - ### 全体 - - {:.input_area} -``` +```R vis_dat(df_lp_kanto) ``` - ### 1変数の可視化 データのばらつきを見るのにはヒストグラム、箱ひげ図を利用します。一変量を対象とした単純な可視化は、変数の変動、特性を理解するのに役立ちます。 @@ -284,35 +580,27 @@ vis_dat(df_lp_kanto) スパイク(峰)を検出するのに効果的です。ヒストグラムは単峰、二峰など多様な形状を取り得ます。 - - {:.input_area} -``` +```R df_lp_kanto %>% ggplot(aes(distance_from_station)) + geom_histogram(bins = 30) ``` - ![](../images/hist_lp_distance_from_station-1.png) - - {:.input_area} -``` +```R df_lp_kanto %>% ggplot(aes(distance_from_station)) + geom_histogram(bins = 30) + facet_wrap(~ .prefecture, ncol = 1) ``` - - - {:.input_area} -``` +```R library(ggridges) ggplot(df_lp_kanto, @@ -323,40 +611,32 @@ ggplot(df_lp_kanto, scale_fill_ds() ``` - ![](../images/hist_lp_distance_from_station_ggridges-1.png) #### 箱ひげ図・バイオリンプロット ### 2変数の可視化 - - - {:.input_area} -``` +```R df_lp_kanto %>% ggplot(aes(use_district, posted_land_price)) + geom_boxplot() ``` - #### 散布図 - - {:.input_area} -``` +```R df_lp_kanto %>% ggplot(aes(distance_from_station, posted_land_price)) + geom_point() ``` - - +![](../images/lp_scatter_distance_from_station_posted_land_price-1.png) {:.input_area} -``` +```R df_lp_kanto %>% ggplot(aes(distance_from_station, acreage)) + geom_point() + @@ -364,6 +644,7 @@ df_lp_kanto %>% scale_y_log10() ``` +![](../images/lp_scatter_distance_from_station_posted_land_price_logscale-1.png) ## 特殊なデータの視覚化 @@ -371,36 +652,19 @@ df_lp_kanto %>% 時系列データを扱うときは、時間のならびの通りに表示させることが肝心です。周期があるものは分割したり重ねてみると良いでしょう。 - - -{:.input_area} -``` -df_beer2018q2 %>% - ggplot(aes(date, expense)) + - geom_point() + - geom_line() + - scale_x_date(date_breaks = "7 days") -``` - - ![](../images/beer_ts-1.png) - - {:.input_area} -``` +```R df_beer2018q2 %>% ggplot(aes(expense)) + geom_histogram(bins = 30) ``` - ### 空間データ - - {:.input_area} -``` +```R sf_lp_kanto <- df_lp_kanto %>% select(posted_land_price, .longitude, .latitude) %>% @@ -413,7 +677,6 @@ ggplot(sf_lp_kanto) + scale_color_viridis_c() ``` - ### 高次元の可視化 @@ -428,42 +691,47 @@ ggplot(sf_lp_kanto) + #### 散布図行列 - - - {:.input_area} -``` +```R df_is_num %>% GGally::ggpairs() ``` - #### 相関行列 - - {:.input_area} -``` +```R df_is_num %>% corrr::correlate() ``` - - - -{:.input_area} -``` -df_is_num %>% - vis_cor() -``` - + ## + ## Correlation method: 'pearson' + ## Missing treated using: 'pairwise.complete.obs' + + ## # A tibble: 11 x 12 + ## rowname posted_land_pri… distance_from_s… acreage depth_ratio + ## + ## 1 posted… NA -0.126 0.00819 0.0375 + ## 2 distan… -0.126 NA 0.0468 -0.0646 + ## 3 acreage 0.00819 0.0468 NA 0.00383 + ## 4 depth_… 0.0375 -0.0646 0.00383 NA + ## 5 number… 0.438 -0.228 -0.0135 0.102 + ## 6 number… 0.543 -0.138 0.0164 0.0428 + ## 7 buildi… 0.212 -0.206 -0.00184 0.294 + ## 8 fronta… 0.0486 -0.0185 0.0330 -0.220 + ## 9 floor_… 0.473 -0.228 0.00763 0.223 + ## 10 attrib… 0.00416 -0.0201 -0.00616 0.0244 + ## 11 night_… 0.0103 -0.172 -0.0202 0.0211 + ## # … with 7 more variables: number_of_floors , + ## # number_of_basement_floors , building_coverage , + ## # frontage_ratio , floor_area_ratio , + ## # attribute_change_selected_land_status , night_population ![](../images/lp_correlation-1.png) - - {:.input_area} -``` +```R df_is_log %>% mutate_all(as.numeric) %>% vis_cor() @@ -473,7 +741,6 @@ all.equal( df_lp_kanto$attribute_change_floor_area_ratio) ``` - #### t-SNE 高次元データの2次元散布図を用いた可視化に利用される @@ -484,16 +751,16 @@ all.equal( ## まとめ -- モデリング、統計分析を行う前にデータを精査することが大事 - - データの特徴を理解することで次のステップにかける時間を減らす、異常を見逃さない、(意図しない)間違いを見逃さない -- 特に可視化の手法を用いることでデータの集約や関係、パターンを見やすくする + - モデリング、統計分析を行う前にデータを精査することが大事 + - データの特徴を理解することで次のステップにかける時間を減らす、異常を見逃さない、(意図しない)間違いを見逃さない + - 特に可視化の手法を用いることでデータの集約や関係、パターンを見やすくする ## 関連項目 -- [次元削減](../03/dimension-reduction) -- [欠損処理](../03/handling-missing-data) -- [変数重要度](../03/feature-selection) + - [次元削減](../03/dimension-reduction) + - [欠損処理](../03/handling-missing-data) + - [変数重要度](../03/feature-selection) ## 参考文献 -- + - diff --git a/_build/01/tidy-data.md b/_build/01/tidy-data.md index 24c3204..dac6b1f 100644 --- a/_build/01/tidy-data.md +++ b/_build/01/tidy-data.md @@ -9,17 +9,12 @@ prev_page: next_page: url: /01/eda title: '探索的データ分析' +output: github_document + comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" --- - -{:.input_area} -```R -source(here::here("R/setup.R")) -``` - - # tidyデータ: 人間にも機械にも優しいデータの記述形式 データが手に入ってすぐにモデルに利用できることはごく稀で、前処理と呼ばれる作業が必要になります。この作業はデータをモデルに流し込む前段階として必ず必要です。それはデータが欠損値や外れ値といった、扱いに困る値を含んでいることもありますが、多くのデータは入力の形式のまま利用可能ではなく、プログラムが処理しやすい形式へ整形することが求められるためです。 @@ -32,27 +27,35 @@ source(here::here("R/setup.R")) 前処理の必要性とその範囲は適用するモデルのタイプによって異なります。 -木ベースのモデル(決定木、ランダムフォレスト)では、特徴量を入力とする複数のステップ関数(閾値を超えた場合に1, そうでなければ0に変換する)の組み合わせによって構成されるため変数のスケールの影響を受けません。しかしロジスティック回帰や部分最小二乗法、リッジ回帰や距離を利用するk-means、主成分分析など、多くのモデルは入力のスケールに敏感で、変数間のスケールを揃える必要があります。 +木ベースのモデル(決定木、ランダムフォレスト)では、特徴量を入力とする複数のステップ関数(閾値を超えた場合に1, +そうでなければ0に変換する)の組み合わせによって構成されるため変数のスケールの影響を受けません。しかしロジスティック回帰や部分最小二乗法、リッジ回帰や距離を利用するk-means、主成分分析など、多くのモデルは入力のスケールに敏感で、変数間のスケールを揃える必要があります。 ### 前処理の必要性1: 重回帰モデル - - {:.input_area} -```R +``` r df_lp_kanto %>% lm(posted_land_price ~ distance_from_station + acreage + night_population, data = .) %>% tidy() ``` + ## # A tibble: 4 x 5 + ## term estimate std.error statistic p.value + ## + ## 1 (Intercept) 620112. 37120. 16.7 1.15e-61 + ## 2 distance_from_station -115. 9.85 -11.7 1.82e-31 + ## 3 acreage 3.41 2.63 1.29 1.95e- 1 + ## 4 night_population -0.103 0.0976 -1.05 2.93e- 1 -`distance_from_staion` の影響が一番強く、`acreage`、`night_population`が続きます。しかし `distance_from_staion` の値が大きすぎて他の変数の効果が小さいように見えます。これは元のスケールの影響を受けているためです。 `distance_from_staion` と `acreage` の1は同じ単位ではありません。偏回帰係数を比較する場合、スケーリングを行っておく必要があります。 - - +`distance_from_staion` の影響が一番強く、`acreage`、`night_population`が続きます。しかし +`distance_from_staion` +の値が大きすぎて他の変数の効果が小さいように見えます。これは元のスケールの影響を受けているためです。 +`distance_from_staion` と `acreage` +の1は同じ単位ではありません。偏回帰係数を比較する場合、スケーリングを行っておく必要があります。 {:.input_area} -```R +``` r df_lp_kanto %>% recipe(posted_land_price ~ distance_from_station + acreage + night_population) %>% step_center(all_predictors()) %>% @@ -64,15 +67,29 @@ df_lp_kanto %>% tidy() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 4 x 5 + ## term estimate std.error statistic p.value + ## + ## 1 (Intercept) 415432. 19034. 21.8 8.65e-103 + ## 2 distance_from_station -226650. 19344. -11.7 1.82e- 31 + ## 3 acreage 24678. 19058. 1.29 1.95e- 1 + ## 4 night_population -20325. 19326. -1.05 2.93e- 1 ### 前処理の必要性2: 変数間のスケールが揃わない主成分分析 [主成分分析](../03/dimension-reduction)では入力に用いる変数間のスケールが標準化されていることが前提です。それは特徴量空間におけるデータ間を距離を利用するためです。そのため、値の範囲が大きく異なる変数があることでその影響を強く受けます。まずは標準化を行わない場合の結果を見てみましょう。 - - {:.input_area} -```R +``` r pca_res <- prcomp(~ distance_from_station + acreage + night_population, data = df_lp_kanto, @@ -80,17 +97,33 @@ pca_res <- scale. = FALSE) # 主成分軸上のSDが大きく異なっていることに注意(単位の影響を強く受けている) pca_res +``` + + ## Standard deviations (1, .., p=3): + ## [1] 326371.254 7270.361 2279.386 + ## + ## Rotation (n x k) = (3 x 3): + ## PC1 PC2 PC3 + ## distance_from_station 0.003184224 0.027579010 -0.99961456 + ## acreage 0.001558732 0.999618272 0.02758408 + ## night_population 0.999993716 -0.001645965 0.00314002 + +{:.input_area} +``` r # 第1主成分のみで累積寄与率が99%を超える summary(pca_res) ``` + ## Importance of components: + ## PC1 PC2 PC3 + ## Standard deviation 3.264e+05 7.27e+03 2.279e+03 + ## Proportion of Variance 9.995e-01 5.00e-04 5.000e-05 + ## Cumulative Proportion 9.995e-01 1.00e+00 1.000e+00 次にあらかじめ標準化したデータを元に主成分分析を行った結果を見ます。 - - {:.input_area} -```R +``` r pca_res <- prcomp(~ distance_from_station + acreage + night_population, data = df_lp_kanto, @@ -98,17 +131,33 @@ pca_res <- scale. = TRUE) # SDが小さくなる pca_res +``` + + ## Standard deviations (1, .., p=3): + ## [1] 1.0884300 0.9949390 0.9085244 + ## + ## Rotation (n x k) = (3 x 3): + ## PC1 PC2 PC3 + ## distance_from_station -0.6939753 0.09743571 0.7133755 + ## acreage -0.2496712 -0.96188952 -0.1115026 + ## night_population 0.6753241 -0.25548939 0.6918544 + +{:.input_area} +``` r # 第2主成分軸まで含めて72%を説明 summary(pca_res) ``` + ## Importance of components: + ## PC1 PC2 PC3 + ## Standard deviation 1.0884 0.9949 0.9085 + ## Proportion of Variance 0.3949 0.3300 0.2751 + ## Cumulative Proportion 0.3949 0.7249 1.0000 ### 前処理の必要性3: kNN - - {:.input_area} -```R +``` r library(FNN) set.seed(12) @@ -124,8 +173,15 @@ df_test <- testing(split_hazard) knn_res <- knn(df_train, df_test, df_train$hazard, k = 2, prob = FALSE) df_test$predict <- knn_res attr(knn_res, "nn.index") %>% as.data.frame() %>% as_tibble() %>% .[21, ] +``` + ## # A tibble: 1 x 2 + ## V1 V2 + ## + ## 1 409 619 +{:.input_area} +``` r # get.knn(df_test %>% select_if(is.numeric), k = 2)["nn.index"] %>% as.data.frame() %>% .[397, ] rec <- @@ -138,20 +194,46 @@ df_train_baked <- rec %>% prep(training = df_train) %>% bake(new_data = df_train) +``` + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + +{:.input_area} +``` r df_test_baked <- rec %>% prep(training = df_train) %>% bake(new_data = df_test) +``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + +{:.input_area} +``` r knn(df_train_baked, df_test_baked, df_train_baked$hazard, k = 2) %>% table() ``` - - + ## . + ## FALSE TRUE + ## 363 42 {:.input_area} -```R +``` r # p1 <- # ggplot(df_train, aes(max_elevation, mean_slope_aspect)) + # geom_point(aes(color = hazard)) @@ -162,11 +244,8 @@ knn(df_train_baked, df_test_baked, df_train_baked$hazard, k = 2) %>% # # plot_grid(p1, p2) ``` - - - {:.input_area} -```R +``` r p2 <- ggplot(df_test, aes(max_elevation, mean_slope_aspect)) + @@ -189,7 +268,6 @@ plot_grid(p3 + p3 + guides(color = FALSE)) ``` - ![](../images/knn_scaling_trouble-1.png) @@ -208,38 +286,32 @@ PLSなどで恩恵がありますが 特徴量の値を0~1の範囲に収める変換をMin-Maxスケーリングと呼びます。 -$$ +\[ \tilde{x} = \frac{x - min(x)}{max(x) - min(x)} -$$ +\] Min-Maxスケーリングを行う際は、数値の取り得る値があらかじめわかっているものが望ましいです。データとして存在しない値や外れ値が与えられることで、その値に引っ張られて相対的な差がなくなってしまう恐れがあるからです。 - - {:.input_area} -```R +``` r # 地価データの「最寄り駅までの距離」の範囲 lp_dist <- df_lp_kanto$distance_from_station range(lp_dist) ``` - - + ## [1] 0 24000 {:.input_area} -```R +``` r lp_dist_minmax <- scale(lp_dist, center = min(lp_dist), scale = (max(lp_dist) - min(lp_dist))) range(lp_dist_minmax) ``` - 全体を考慮 - - {:.input_area} -```R +``` r p1 <- ggplot(df_lp_kanto, aes(x = 0, y = distance_from_station)) + geom_violin(color = ds_col(1)) @@ -252,32 +324,29 @@ p2 <- plot_grid(p1, p2) ``` - -- 外れ値が含まれていない場合に良い -- 外れ値の情報を活用したい時には適さない + - 外れ値が含まれていない場合に良い + - 外れ値の情報を活用したい時には適さない ### 標準化 -標準化 (standardization)... 平均0、分散(標準偏差)1になる。 +標準化 (standardization)… 平均0、分散(標準偏差)1になる。 -$$ +\[ \tilde{x} = \frac{x- mean(x)}{sqrt(var(x))} -$$ +\] -- 複数の変数に対して行うことで比較が可能になる (平均0, 標準偏差1) -- 範囲スケーリング... 最大値、最小値を利用する -- 共通の「単位」をもつように扱いたい場合に有効 - - 距離または内積を利用する --> KNN, SVMs - - ペナルティを課すため --> lasso, ridge + - 複数の変数に対して行うことで比較が可能になる (平均0, 標準偏差1) + - 範囲スケーリング… 最大値、最小値を利用する + - 共通の「単位」をもつように扱いたい場合に有効 + - 距離または内積を利用する –\> KNN, SVMs + - ペナルティを課すため –\> lasso, ridge を標準化 (standardization) と呼びます。具体的には変数の取りうる値を平均0、分散1に変換する処理です。 - - {:.input_area} -```R +``` r # スケーリングでは分布は変わらない range(df_lp_kanto$posted_land_price) p_base <- @@ -293,7 +362,6 @@ df_lp_kanto %>% # hist(scale(df_lp_kanto$posted_land_price)[, 1]) ``` - リッジ回帰、主成分分析では変数の標準化が前提となっている ### 正規化 @@ -308,46 +376,68 @@ df_lp_kanto %>% データの表現方法を変える - - {:.input_area} -```R +``` r # 地価データにおける駅からの距離 # 平均と分散 df_lp_kanto %>% summarise(mean = mean(distance_from_station), sd = sd(distance_from_station)) +``` + ## # A tibble: 1 x 2 + ## mean sd + ## + ## 1 1566. 1965. + +{:.input_area} +``` r # center, scale引数はいずれも既定でTRUEです lp_dist_scaled <- c(scale(df_lp_kanto$distance_from_station, center = TRUE, scale = TRUE)) mean(lp_dist_scaled) # 限りなく0に近くなる -sd(lp_dist_scaled) # 分散は1 ``` + ## [1] 3.056636e-17 +{:.input_area} +``` r +sd(lp_dist_scaled) # 分散は1 +``` + ## [1] 1 {:.input_area} -```R +``` r diff(range(lp_dist)) +``` + + ## [1] 24000 + +{:.input_area} +``` r median(lp_dist) +``` + ## [1] 1000 + +{:.input_area} +``` r diff(range(c(scale(lp_dist)))) ``` + ## [1] 12.215 -標準地からの鉄道駅までの距離の中央値は900で、最小値は0 (近接の場合は0が与えられる)、最大値は24000です。この特徴量を標準化するとその差は14.02にまで縮まります。 +標準地からの鉄道駅までの距離の中央値は900で、最小値は0 +(近接の場合は0が与えられる)、最大値は24000です。この特徴量を標準化するとその差は14.02にまで縮まります。 距離の これらの手法は、対数変換と異なりデータの分布には影響しないことが特徴です。 - - {:.input_area} -```R +``` r p1 <- ggplot(df_lp_kanto, aes(distance_from_station)) + @@ -361,31 +451,33 @@ p2 <- plot_grid(p1, p2, ncol = 2) ``` +![](../images/scale_histogram_comared-1.png) ## データ浄化 -1. 情報を含まない列の削除 - - 分散が0の説明変数 (constant cols) - - 重複した説明変数を一つに - - 多重共線性 - - 相関係数が1である説明変数の組 (perfectly correlated cols) をユニークに - - データリーク +1. 情報を含まない列の削除 + - 分散が0の説明変数 (constant cols) + - 重複した説明変数を一つに + - 多重共線性 + - 相関係数が1である説明変数の組 (perfectly correlated cols) をユニークに + - データリーク -データセットに含まれる列のうち、`attribute_change_forest_law` と `attribute_change_parks_law` は論理型の変数でありながら1値しか持たない(分散が0)ものでした。 +データセットに含まれる列のうち、`attribute_change_forest_law` と +`attribute_change_parks_law` は論理型の変数でありながら1値しか持たない(分散が0)ものでした。 ### 外れ値 ### 欠損処理 -![欠損処理](../03/handling-missing-data) +[欠損処理](../03/handling-missing-data) ### 不要な列の削除 特徴量選択の文脈においてはフィルタ法 -... 情報量がない列 +… 情報量がない列 単一の変数についてと、変数間の関係をそれぞれ調べることが重要になります。 @@ -395,41 +487,112 @@ plot_grid(p1, p2, ncol = 2) -- 変数内 - - 単一の値のみが含まれる変数 (分散0) -- 変数間 - - 同一の値の組み合わせ - - 相関係数が1になる -- データセット全体 - - 完全に欠損する行・列 - + - 変数内 + - 単一の値のみが含まれる変数 (分散0) + - 変数間 + - 同一の値の組み合わせ + - 相関係数が1になる + - データセット全体 + - 完全に欠損する行・列 + {:.input_area} -```R +``` r df_lp_kanto %>% skimr::skim_to_list() ``` - -`attribute_change_forest_law` と `attribute_change_parks_law` の列は二値データを持つことができる変数ですが、いずれも一つの値しか記録されていません。変数内での分散は0となり、情報を持っていないのと同義です(標準化も適用できません)。そのためこれらの値はモデル構築の前にデータから削除しても問題ないと考えられます。 + ## $character + ## # A tibble: 14 x 8 + ## variable missing complete n min max empty n_unique + ## * + ## 1 .prefecture 0 8476 8476 3 4 0 7 + ## 2 administrative_area_c… 0 8476 8476 5 5 0 329 + ## 3 building_structure 220 8256 8476 2 8 0 130 + ## 4 configuration 7454 1022 8476 2 3 0 2 + ## 5 current_use 0 8476 8476 2 14 0 70 + ## 6 fire_area 4859 3617 8476 2 2 0 2 + ## 7 forest_law 8450 26 8476 3 3 0 1 + ## 8 name_of_nearest_stati… 0 8476 8476 1 14 0 1481 + ## 9 parks_law 8465 11 8476 7 7 0 2 + ## 10 proximity_with_transp… 8308 168 8476 2 6 0 3 + ## 11 surrounding_present_u… 0 8476 8476 15 22 0 4397 + ## 12 urban_planning_area 0 8476 8476 2 3 0 4 + ## 13 usage_description 7552 924 8476 1 8 0 40 + ## 14 use_district 515 7961 8476 2 3 0 12 + ## + ## $integer + ## # A tibble: 6 x 12 + ## variable missing complete n mean sd p0 p25 p50 p75 p100 + ## * + ## 1 .row_id 0 8476 8476 " 18… " … 13534 1640… " 18… " 20… " 23… + ## 2 acreage 0 8476 8476 " … " … 46 " 1… " … " … "413… + ## 3 distanc… 0 8476 8476 " 1… " … 0 " 5… " 1… " 1… " 24… + ## 4 number_… 0 8476 8476 " … " … 0 " … " … " … " … + ## 5 number_… 0 8476 8476 " … " … 0 " … " … " … " … + ## 6 posted_… 0 8476 8476 "415… 1766… 613 "599… "155… "308… " … + ## # … with 1 more variable: hist + ## + ## $logical + ## # A tibble: 17 x 6 + ## variable missing complete n mean count + ## * + ## 1 attribute_change_acreage 0 8476 8476 "0.003… FAL: 8450, TRU:… + ## 2 attribute_change_address 0 8476 8476 "0.007… FAL: 8411, TRU:… + ## 3 attribute_change_buildi… 0 8476 8476 0.00024 FAL: 8474, TRU:… + ## 4 attribute_change_buildi… 0 8476 8476 "0.008… FAL: 8406, TRU:… + ## 5 attribute_change_curren… 0 8476 8476 "0.008… FAL: 8402, TRU:… + ## 6 attribute_change_distan… 0 8476 8476 "0.005… FAL: 8431, TRU:… + ## 7 attribute_change_fire_a… 0 8476 8476 "0.007… FAL: 8417, TRU:… + ## 8 attribute_change_floor_… 0 8476 8476 0.00024 FAL: 8474, TRU:… + ## 9 attribute_change_forest… 0 8476 8476 "0 … FAL: 8476, NA: 0 + ## 10 attribute_change_parks_… 0 8476 8476 "0 … FAL: 8476, NA: 0 + ## 11 attribute_change_suppli… 0 8476 8476 "0.005… FAL: 8429, TRU:… + ## 12 attribute_change_urban_… 0 8476 8476 0.00012 FAL: 8475, TRU:… + ## 13 attribute_change_use_di… 0 8476 8476 0.00012 FAL: 8475, TRU:… + ## 14 common_surveyed_position 0 8476 8476 "0.071… FAL: 7870, TRU:… + ## 15 gas_facility 0 8476 8476 "0.78 … TRU: 6616, FAL:… + ## 16 sewage_facility 0 8476 8476 "0.92 … TRU: 7775, FAL:… + ## 17 water_facility 0 8476 8476 "0.99 … TRU: 8427, FAL:… + ## + ## $numeric + ## # A tibble: 8 x 12 + ## variable missing complete n mean sd p0 p25 p50 p75 p100 + ## * + ## 1 .latitu… 0 8476 8476 " … " … " 3… " … " … " … " … + ## 2 .longit… 0 8476 8476 " … " … " 13… " … " 1… " … " 1… + ## 3 attribu… 0 8476 8476 " … " … " … " … " … " … " … + ## 4 buildin… 0 8476 8476 " … " … " … " … " … " … " … + ## 5 depth_r… 0 8476 8476 " … " … " … " … " … " … " … + ## 6 floor_a… 0 8476 8476 " … " 1… " … " … " 2… " … " 13… + ## 7 frontag… 0 8476 8476 " … " … " … " … " … " … " … + ## 8 night_p… 0 8476 8476 2594… "2e+… "321… "118… "2e+… "341… "9e+… + ## # … with 1 more variable: hist + +`attribute_change_forest_law` と `attribute_change_parks_law` +の列は二値データを持つことができる変数ですが、いずれも一つの値しか記録されていません。変数内での分散は0となり、情報を持っていないのと同義です(標準化も適用できません)。そのためこれらの値はモデル構築の前にデータから削除しても問題ないと考えられます。 情報量がゼロ(分散ゼロ) 数値データでは稀ですが、論理値変数やカテゴリ変数に潜んでいる、こうした1種類の値しか取らないデータは除外しましょう。 - - - {:.input_area} -```R +``` r +# 分散ゼロ scale(c(0, 0, 0), scale = TRUE) ``` - - + ## [,1] + ## [1,] NaN + ## [2,] NaN + ## [3,] NaN + ## attr(,"scaled:center") + ## [1] 0 + ## attr(,"scaled:scale") + ## [1] 0 {:.input_area} -```R +``` r df_lp_kanto_clean <- df_lp_kanto %>% verify(ncol(.) == 45) %>% @@ -441,6 +604,7 @@ df_lp_kanto_clean <- dim(df_lp_kanto_clean) ``` + ## [1] 8476 43 @@ -448,26 +612,33 @@ dim(df_lp_kanto_clean) どうやら違うようです。 - - {:.input_area} -```R +``` r df_lp_kanto %>% distinct(attribute_change_urban_planning_area, attribute_change_use_district) ``` + ## # A tibble: 3 x 2 + ## attribute_change_urban_planning_area attribute_change_use_district + ## + ## 1 FALSE FALSE + ## 2 FALSE TRUE + ## 3 TRUE FALSE 分散が0に近い変数を削除する 変数の数は減りますが、重要な変数を削除してしまう恐れがあります。この作業は慎重に - - {:.input_area} -```R +``` r dim(df_lp_kanto_clean) +``` + ## [1] 8476 43 + +{:.input_area} +``` r nzv_filter <- df_lp_kanto_clean %>% recipe(~ .) %>% @@ -478,26 +649,55 @@ filtered_te <- prep() %>% juice() %>% verify(ncol(.) == 28) +``` + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? +{:.input_area} +``` r dim(filtered_te) +``` + + ## [1] 8476 28 +{:.input_area} +``` r # 削除される列がどのような値を持っているか確認して df_lp_kanto_clean %>% select(names(df_lp_kanto_clean)[!names(df_lp_kanto_clean) %in% names(filtered_te)]) %>% glimpse() +``` + ## Observations: 8,476 + ## Variables: 15 + ## $ usage_description "別荘", NA, NA, "別荘", NA, … + ## $ attribute_change_supplied_facility FALSE, FALSE, FALSE, FAL… + ## $ water_facility TRUE, TRUE, TRUE, TRUE, … + ## $ forest_law NA, NA, NA, NA, NA, NA, … + ## $ attribute_change_floor_area_ratio FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_selected_land_status 1, 1, 1, 1, 1, 1, 1, 1, … + ## $ attribute_change_address FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_acreage FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_current_use FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_building_structure FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_distance_from_station FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_use_district FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_fire_area FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_urban_planning_area FALSE, FALSE, FALSE, FAL… + ## $ attribute_change_building_coverage FALSE, FALSE, FALSE, FAL… + +{:.input_area} +``` r # 重要ではなさそうなので削除 df_lp_kanto_clean <- filtered_te ``` - - - - - - {:.input_area} -```R +``` r # 変わらん(正しい) # df_hazard_kys %>% # as.data.frame() %>% @@ -505,17 +705,54 @@ df_lp_kanto_clean <- filtered_te # dim() ``` - 相関係数が高い変数を 多重共線性の問題を引き起こします。 いずれかの変数だけを利用するようにしましょう。 +{:.input_area} +``` r +library(GGally) +``` + ## Registered S3 method overwritten by 'GGally': + ## method from + ## +.gg ggplot2 {:.input_area} -```R +``` r +df_lp_kanto_clean %>% + select(-starts_with(".")) %>% + select_if(is.numeric) %>% + corrr::correlate() +``` + + ## + ## Correlation method: 'pearson' + ## Missing treated using: 'pairwise.complete.obs' + + ## # A tibble: 10 x 11 + ## rowname posted_land_pri… distance_from_s… acreage depth_ratio + ## + ## 1 posted… NA -0.126 0.00819 0.0375 + ## 2 distan… -0.126 NA 0.0468 -0.0646 + ## 3 acreage 0.00819 0.0468 NA 0.00383 + ## 4 depth_… 0.0375 -0.0646 0.00383 NA + ## 5 number… 0.438 -0.228 -0.0135 0.102 + ## 6 number… 0.543 -0.138 0.0164 0.0428 + ## 7 buildi… 0.212 -0.206 -0.00184 0.294 + ## 8 fronta… 0.0486 -0.0185 0.0330 -0.220 + ## 9 floor_… 0.473 -0.228 0.00763 0.223 + ## 10 night_… 0.0103 -0.172 -0.0202 0.0211 + ## # … with 6 more variables: number_of_floors , + ## # number_of_basement_floors , building_coverage , + ## # frontage_ratio , floor_area_ratio , night_population + +{:.input_area} +``` r +library(GGally) + df_lp_kanto_clean %>% select(-starts_with(".")) %>% select_if(is.numeric) %>% @@ -526,27 +763,53 @@ df_lp_kanto_clean %>% select(-starts_with(".")) %>% select_if(is.numeric) %>% ggcorr(label = TRUE, label_round = 2) - -df_hazard_kys %>% - select(-block_no, -longitude, -latitude) %>% - select_if(is.numeric) %>% - ggcorr(label = TRUE, label_round = 2) ``` - - +![](../images/lp_correlation_ggally-1.png) {:.input_area} -```R +``` r # df_lp_kanto では不適切 - df_lp_kanto_clean %>% recipe(~ .) %>% step_corr(all_numeric(), -starts_with("."), threshold = 0.9) %>% prep() %>% juice() %>% verify(ncol(.) == 28L) +``` + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 8,476 x 28 + ## .row_id .prefecture administrative_… posted_land_pri… name_of_nearest… + ## + ## 1 13534 群馬県 10425 4150 万座・鹿沢口 + ## 2 13604 群馬県 10426 39000 長野原草津口 + ## 3 13605 群馬県 10426 56800 長野原草津口 + ## 4 13613 群馬県 10424 2700 羽根尾 + ## 5 13620 群馬県 10424 12100 羽根尾 + ## 6 13621 群馬県 10426 29800 長野原草津口 + ## 7 13640 群馬県 10424 16400 群馬大津 + ## 8 13755 群馬県 10382 17400 下仁田 + ## 9 13758 群馬県 10382 25500 下仁田 + ## 10 13765 群馬県 10382 23300 下仁田 + ## # … with 8,466 more rows, and 23 more variables: + ## # distance_from_station , acreage , current_use , + ## # building_structure , gas_facility , sewage_facility , + ## # proximity_with_transportation_facility , depth_ratio , + ## # number_of_floors , number_of_basement_floors , + ## # use_district , building_coverage , configuration , + ## # surrounding_present_usage , fire_area , + ## # urban_planning_area , parks_law , frontage_ratio , + ## # floor_area_ratio , common_surveyed_position , + ## # .longitude , .latitude , night_population +{:.input_area} +``` r # 閾値を低くする df_lp_kanto_clean %>% recipe(~ .) %>% @@ -556,6 +819,35 @@ df_lp_kanto_clean %>% verify(ncol(.) == 27L) ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 8,476 x 27 + ## .row_id .prefecture administrative_… posted_land_pri… name_of_nearest… + ## + ## 1 13534 群馬県 10425 4150 万座・鹿沢口 + ## 2 13604 群馬県 10426 39000 長野原草津口 + ## 3 13605 群馬県 10426 56800 長野原草津口 + ## 4 13613 群馬県 10424 2700 羽根尾 + ## 5 13620 群馬県 10424 12100 羽根尾 + ## 6 13621 群馬県 10426 29800 長野原草津口 + ## 7 13640 群馬県 10424 16400 群馬大津 + ## 8 13755 群馬県 10382 17400 下仁田 + ## 9 13758 群馬県 10382 25500 下仁田 + ## 10 13765 群馬県 10382 23300 下仁田 + ## # … with 8,466 more rows, and 22 more variables: + ## # distance_from_station , acreage , current_use , + ## # building_structure , gas_facility , sewage_facility , + ## # proximity_with_transportation_facility , depth_ratio , + ## # number_of_floors , number_of_basement_floors , + ## # use_district , building_coverage , configuration , + ## # surrounding_present_usage , fire_area , + ## # urban_planning_area , parks_law , frontage_ratio , + ## # common_surveyed_position , .longitude , .latitude , + ## # night_population テキストデータの前処理については[別の章](../02/text)で解説します。 @@ -567,11 +859,14 @@ df_lp_kanto_clean %>% ## 関連項目 -- [次元削減](../03/dimension-reduction) -- [特徴量選択](../03/feature-selection) + - [次元削減](../03/dimension-reduction) + - [特徴量選択](../03/feature-selection) ## 参考文献 -- Max Kuhn and Kjell Johnson (2013). Applied Predictive Modeling. (Springer) -- 本橋智光 (2018). 前処理大全 (技術評論社) -- Max Kuhn and Kjell Johnson (2019).[Feature Engineering and Selection: A Practical Approach for Predictive Models](https://bookdown.org/max/FES/) (CRC Press) + - Max Kuhn and Kjell Johnson (2013). Applied Predictive Modeling. + (Springer) + - 本橋智光 (2018). 前処理大全 (技術評論社) + - Max Kuhn and Kjell Johnson (2019).[Feature Engineering and + Selection: A Practical Approach for Predictive + Models](https://bookdown.org/max/FES/) (CRC Press) diff --git a/_build/01/tidymodels-workflow.md b/_build/01/tidymodels-workflow.md index 28e76ce..0c903ff 100644 --- a/_build/01/tidymodels-workflow.md +++ b/_build/01/tidymodels-workflow.md @@ -9,23 +9,18 @@ prev_page: next_page: url: /02/readme title: '特徴量エンジニアリング' +output: github_document + comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" --- - -{:.input_area} -``` -source(here::here("R/core_pkgs.R")) -source(here::here("R/prepared_landprice.R")) # df_lp_kanto_clean -``` - - # モデルワークフロー 前章までにデータ分析のワークフローを紹介し、実際にデータの可視化や前処理を行いました。残るステップはモデルの実行です。しかし、EDAと前処理、モデルの実行は繰り返しの作業であることは最初に述べた通りです。最初に構築したモデルが最終的なレポートに使われることはなく、モデルの結果を評価し、そこからさらなる処理を加えていくことでモデルを磨き上げていくのが一般的です。 -ここで、Max Kuhn and Kjell Johnson (2019) が示したモデルワークフローを紹介します。ここにはモデルの実行だけでなく、[モデルの評価](../03/model-performance)や[ハイパーパラメータの探索](../03/parameter-tuning)と呼ばれる作業の他、EDAや特徴量エンジニアリングが含まれます。順を追って解説します。 +ここで、Max Kuhn and Kjell Johnson (2019) +が示したモデルワークフローを紹介します。ここにはモデルの実行だけでなく、[モデルの評価](../03/model-performance)や[ハイパーパラメータの探索](../03/parameter-tuning)と呼ばれる作業の他、EDAや特徴量エンジニアリングが含まれます。順を追って解説します。 ![](../images/modeling-process.png) @@ -42,11 +37,11 @@ iの段階でモデルの汎化能力を元に最終テストが行われまし モデルの開発過程を通じて考慮すべきこととして以下のものがあります。 -- 過学習への対策としての[データ分割](../03/data-splitting) -- モデルの[汎化性能を評価するための指標](../03/model-performance)の選択 -- 複数のモデルによる比較と[特徴量の選択](../03/feature-selection) -- [ハイパーパラメータの探索と調整](../03/parameter-tuning) -- [モデルの解釈](../03/interpretability) + - 過学習への対策としての[データ分割](../03/data-splitting) + - モデルの[汎化性能を評価するための指標](../03/model-performance)の選択 + - 複数のモデルによる比較と[特徴量の選択](../03/feature-selection) + - [ハイパーパラメータの探索と調整](../03/parameter-tuning) + - [モデルの解釈](../03/interpretability) これらについては本書の後半で解説します。 @@ -54,22 +49,24 @@ iの段階でモデルの汎化能力を元に最終テストが行われまし ここでは地価公示データを例に、モデル実行と汎化性能の評価までを一貫して行う例を見ていきたいと思います。本来はここにパラメータ探索の作業も含まれますが省略します。なお探索的データ分析、クレジングによりデータをモデルに投入するための用意ができているものとします。 -モデル構築に取り掛かる前に、まずは問題と性能評価のための方針を改めて確認しておきましょう。地価公示データの問題は、地価公示価格に関連する土地的な要因をもとに、地価公示価格を予測するという回帰のタスクです。あらかじめ価格についてのデータは `posted_land_price` の列に記録されているため、教師付き学習になります。 +モデル構築に取り掛かる前に、まずは問題と性能評価のための方針を改めて確認しておきましょう。地価公示データの問題は、地価公示価格に関連する土地的な要因をもとに、地価公示価格を予測するという回帰のタスクです。あらかじめ価格についてのデータは +`posted_land_price` の列に記録されているため、教師付き学習になります。 ### 評価指標の選択 -回帰問題での典型的な評価指標として、決定係数 (coefficient of determination: RSQ, $R^2$)や二乗平均平方根誤差 (Root Mean Square Error: RMSE) が利用されます。これらの指標についての説明は後述しますが、ここでは決定係数、RMSEに加え平均絶対誤差 (Mean Absolute Error: MAE) を性能評価のための指標として選択します。 +回帰問題での典型的な評価指標として、決定係数 (coefficient of determination: RSQ, +\(R^2\))や二乗平均平方根誤差 (Root Mean Square Error: RMSE) +が利用されます。これらの指標についての説明は後述しますが、ここでは決定係数、RMSEに加え平均絶対誤差 +(Mean Absolute Error: MAE) +を性能評価のための指標として選択します。 決定係数は回帰モデルのあてはまりの良さを判断する目安(説明変数が目的変数をどれくらい説明できているか)として利用され、1に近いほど当てはまりが良いとします。RMSEは個々の予測値の誤差を2乗してから平均して、平方根をとったものです。常に正の値となり、モデルの誤差の大きさを示す指数となります。値が0に近いほど0に近いほど回帰モデルの性能が高いことを示します。またMAEはモデルの平均的な誤差の大きさを示す指標として利用でき、外れ値の影響を受けにくいとされます。この指標もRMSEと一緒で0に近いほど回帰モデルの性能が高いと判断します。 - - {:.input_area} -``` +```R reg_perf_metrics <- metric_set(rmse, rsq, mae) ``` - @@ -80,14 +77,14 @@ reg_perf_metrics <- metric_set(rmse, rsq, mae) 構築したモデルが機能するのか、出力される値に対する評価を事前にしておく必要があります。 -モデルの運用段階では、モデルは未知のデータに対する予測・分類を行わなくてはいけないですが、この段階で未知のデータは手に入れられません。そこで、手元のデータセットをモデルの学習用と性能評価用に分割し、評価するという方法を取るのが一般的です。モデルの学習に用いられるデータセットを分析セット (analysis set)、モデルの学習中には利用されず、汎化性能を評価するために用いるデータセットを評価セット (assessment set)と呼びます(訓練データ、テストデータ)。 +モデルの運用段階では、モデルは未知のデータに対する予測・分類を行わなくてはいけないですが、この段階で未知のデータは手に入れられません。そこで、手元のデータセットをモデルの学習用と性能評価用に分割し、評価するという方法を取るのが一般的です。モデルの学習に用いられるデータセットを分析セット +(analysis set)、モデルの学習中には利用されず、汎化性能を評価するために用いるデータセットを評価セット (assessment +set)と呼びます(訓練データ、テストデータ)。 データ分割の方法は[後の章](../03/data-splitting)で述べるように、データのサイズや構造に応じて柔軟に対応しなければなりませんが、ここではHold-out検証と呼ばれる、データセットをランダムに分割する方法を採用します。次のコードで、データセットの3/4を分析セットに、残りをテストセットとして振り分けます。 - - {:.input_area} -``` +```R # 分析セットと評価セット set.seed(123) lp_split <- @@ -97,40 +94,31 @@ df_lp_train <- training(lp_split) df_lp_test <- testing(lp_split) ``` - ここでデータの分割が正しくできているか、確認のために可視化を行うことが望ましいです。例えば、データ分割をランダムに行ったと言えどデータに偏りがあるかもしれません(ここではグループや空間的なデータのばらつきは無視しています。本来は考慮しなくては良い性能が出なかったり、データ漏洩になる恐れがあります)。分析セットと評価セットのデータの分布やカテゴリデータの場合であれば均等な割合で抽出されているか、時系列データにおいて未来のデータが分析セットに混在していないか、注意深く確認するべきでしょう。 ### 初期モデルの作成 -地価公示データを使って、地価公示標準地の土地の面積と位置(緯度と経度)が価格に影響するという単純なモデルを考えてみます。これは複数の説明変数によって目的変数を説明しようという重回帰モデルになります。ここでは線形回帰モデルとサポートベクトルマシン (SVM) を用いてモデルを作成し比較します。 +地価公示データを使って、地価公示標準地の土地の面積と位置(緯度と経度)が価格に影響するという単純なモデルを考えてみます。これは複数の説明変数によって目的変数を説明しようという重回帰モデルになります。ここでは線形回帰モデルとサポートベクトルマシン +(SVM) を用いてモデルを作成し比較します。 -$$ +\[ land\_price = \beta_0 + \beta_1 acreage + \beta_2 longitude + \beta_3 latitude + \epsilon -$$ - - +\] {:.input_area} -``` +```R mod_formula <- formula(posted_land_price ~ acreage + .longitude + .latitude) ``` - - - {:.input_area} -``` +```R # モデルを定義... 線形回帰モデル spec_lm <- linear_reg() %>% set_engine(engine = "lm") ``` - - - -{:.input_area} -``` +```R ggplot() + geom_sf(data = ne_knt, fill = "transparent") + geom_sf(data = df_lp_kanto_clean %>% @@ -144,14 +132,10 @@ ggplot() + scale_color_viridis_c() ``` - ![](../images/land_price_kanto_log-1.png) - - - {:.input_area} -``` +```R lp_simple_recipe <- df_lp_train %>% recipe(mod_formula) %>% @@ -171,11 +155,15 @@ df_baked %>% glimpse() ``` - - + ## Observations: 2,119 + ## Variables: 4 + ## $ posted_land_price 3.618048, 4.361728, 4.732394, 4.552668, 4.545… + ## $ acreage 2.501059, 2.260071, 2.089905, 2.711807, 2.503… + ## $ .longitude 2.141570, 2.142670, 2.142675, 2.142695, 2.142… + ## $ .latitude 1.561926, 1.560818, 1.559397, 1.560245, 1.559… {:.input_area} -``` +```R fit_lm <- fit( spec_lm, @@ -183,23 +171,41 @@ fit_lm <- data = df_lp_train) tidy(fit_lm) +``` + ## # A tibble: 4 x 5 + ## term estimate std.error statistic p.value + ## + ## 1 (Intercept) 218. 13.1 16.7 4.00e- 61 + ## 2 acreage -0.159 0.0160 -9.93 4.34e- 23 + ## 3 .longitude -54.2 6.17 -8.78 2.15e- 18 + ## 4 .latitude -61.7 1.60 -38.6 1.17e-292 + +{:.input_area} +```R fit_lm %>% predict(new_data = df_lp_train) %>% bind_cols(df_lp_train) %>% reg_perf_metrics(truth = posted_land_price, estimate = .pred) +``` + ## # A tibble: 3 x 3 + ## .metric .estimator .estimate + ## + ## 1 rmse standard 0.488 + ## 2 rsq standard 0.240 + ## 3 mae standard 0.360 + +{:.input_area} +```R # modelr::rmse(fit_lm$fit, df_lp_train) # glance(fit_lm$fit) ``` - 分析セットでのRMSEは0.488、決定係数は0.211となり、まだ適合不足のような結果です。今度は同じ特徴量セットで、SVMを実行してみます。精度はどう変化するでしょうか。 - - {:.input_area} -``` +```R mod_svm <- svm_rbf(mode = "regression", cost = 1) @@ -216,21 +222,31 @@ fit_lm %>% reg_perf_metrics(truth = posted_land_price, estimate = .pred) ``` + ## # A tibble: 3 x 3 + ## .metric .estimator .estimate + ## + ## 1 rmse standard 0.236 + ## 2 rsq standard 0.827 + ## 3 mae standard 0.154 線形回帰の結果よりも精度が向上しました。この特徴量セットでは線形回帰モデルよりもSVMの方がパフォーマンスが良さそうということがわかりました。SVMではさらにハイパーパラメータを最適化する必要がありますが、ここではその手順を省略し、このモデルを用いて最後のモデル評価へと進みます。 ### モデル評価 - - {:.input_area} -``` +```R fit_lm %>% predict(new_data = df_baked) %>% bind_cols(df_baked) %>% reg_perf_metrics(truth = posted_land_price, estimate = .pred) ``` + ## # A tibble: 3 x 3 + ## .metric .estimator .estimate + ## + ## 1 rmse standard 0.251 + ## 2 rsq standard 0.799 + ## 3 mae standard 0.167 RMSEは0.252、決定係数は0.797と最初のモデルとしては十分な結果が得られたかと思います。価格に対して土地の面積が効くという仮説は間違っていないようです(他の変数についても試す必要がありますが)。しかしここがスタート地点です。この結果を良くも悪くも捉えすぎないようにしましょう。改善すべきポイントはたくさんあるはずです。重要なのはモデルを当てはめるための仮説とデータ変換と、そして実行と性能評価の過程がシームレスに繋がっていることです。次の章以降では、地価公示データをはじめとしたサンプルデータでの特徴量エンジニアリングの方法を探していきます。 @@ -238,11 +254,15 @@ RMSEは0.252、決定係数は0.797と最初のモデルとしては十分な結 ## 関連項目 -- [データ分割](../03/data-splitting) -- [モデルの性能評価](../03/model-performance) + - [データ分割](../03/data-splitting) + - [モデルの性能評価](../03/model-performance) ## 参考文献 -- Aurélien Géron (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow (O'Reilly) (**翻訳** 長尾高弘訳 (2018). scikit-learnとTensorFlowによる実践機械学習 (オライリー)) -- 有賀康顕、中山 心太、西林孝 (2018). 仕事ではじめる機械学習 (オライリー) -- Max Kuhn and Kjell Johnson (2019). [Feature Engineering and Selection: A Practical Approach for Predictive Models](https://bookdown.org/max/FES/) (CRC Press) + - Aurélien Géron (2017). Hands-On Machine Learning with Scikit-Learn + and TensorFlow (O’Reilly) (**翻訳** 長尾高弘訳 (2018). + scikit-learnとTensorFlowによる実践機械学習 (オライリー)) + - 有賀康顕、中山 心太、西林孝 (2018). 仕事ではじめる機械学習 (オライリー) + - Max Kuhn and Kjell Johnson (2019). [Feature Engineering and + Selection: A Practical Approach for Predictive + Models](https://bookdown.org/max/FES/) (CRC Press) diff --git a/_build/02/categorical.md b/_build/02/categorical.md index ee6205e..8b79453 100644 --- a/_build/02/categorical.md +++ b/_build/02/categorical.md @@ -9,30 +9,24 @@ prev_page: next_page: url: /02/text title: 'テキストデータの取り扱い' +output: github_document + comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" --- - - -{:.input_area} -```R -source(here::here("R/setup.R")) -``` - - # カテゴリデータの取り扱い -項目やラベルを区別するために与えられる、文字列または数値の集合をカテゴリと呼びます。ここではカテゴリ変数に含まれる値を水準と呼びます。カテゴリ変数の例として、地名や行政名、職業などがあります。カテゴリの特徴は有限の集団であることで、数値への置き換えも可能です。しかしカテゴリには通常、大小関係はありません。すなわち名義尺度です。このようなデータは非順序データ (non-ordinal data) と呼ばれます。非順序データに対して、大小の区分のあるカテゴリデータもあります。例えば、大相撲の番付には明確な階級があります(横綱、大関、関脇、小結、前頭、これらはさらに幕内というカテゴリに属します。また幕内の下に十両が存在します)。これは順序データ (ordinal data) です。順序データの場合、さらに水準に対して「重み」が存在する可能性があります。 +項目やラベルを区別するために与えられる、文字列または数値の集合をカテゴリと呼びます。ここではカテゴリ変数に含まれる値を水準と呼びます。カテゴリ変数の例として、地名や行政名、職業などがあります。カテゴリの特徴は有限の集団であることで、数値への置き換えも可能です。しかしカテゴリには通常、大小関係はありません。すなわち名義尺度です。このようなデータは非順序データ +(non-ordinal data) +と呼ばれます。非順序データに対して、大小の区分のあるカテゴリデータもあります。例えば、大相撲の番付には明確な階級があります(横綱、大関、関脇、小結、前頭、これらはさらに幕内というカテゴリに属します。また幕内の下に十両が存在します)。これは順序データ +(ordinal data) +です。順序データの場合、さらに水準に対して「重み」が存在する可能性があります。 カテゴリは「どれだけ違うか」ではなく「値が異なる」ことが重要な判断基準となります。すなわち、観客数5000人と観客数1000人はその差を求めることができ、カテゴリとして扱うより数値として扱うべきということになります。一方で、数値のように扱える郵便番号などは数値として扱うことは避けるべきです。これらは数値であっても大小関係や連続的な意味をもたないためです。 カテゴリは時に大規模になります。顧客データを例にとると、ユーザを識別するIDはサービスを利用するユーザ数が増えれば増えるほど大きくなり、計算コストが高くなってしまいます。そこでより多くのカテゴリ数に対応できる特徴量エンジニアリングが求められます。 -ここでは順序のない・あるカテゴリを扱うための特徴量エンジニアリングの手法を紹介します。まず、ダミー変数化はカテゴリデータに対して広く利用されるものでいくつかの形態があります。次に、より多くのカテゴリ数や、新たなカテゴリの水準に対応する方法としてビンカウンティングなどを取り扱います[^1]。 - -[^1]: カテゴリを数値に変換する処理は全般的にエンコーディングと呼ばれます。これは、カテゴリが持つ情報をエンコードして数値に変換する作業を踏襲した命名です。 - - +ここでは順序のない・あるカテゴリを扱うための特徴量エンジニアリングの手法を紹介します。まず、ダミー変数化はカテゴリデータに対して広く利用されるものでいくつかの形態があります。次に、より多くのカテゴリ数や、新たなカテゴリの水準に対応する方法としてビンカウンティングなどを取り扱います\[1\]。 {:.input_area} ```R @@ -46,23 +40,39 @@ df <- add_count(feature) ``` - ## カテゴリデータの例 地価公示価格データは多くのカテゴリ変数をもっています。 - - {:.input_area} ```R df_lp_kanto %>% select_if(is.character) ``` - -土砂災害・雪崩メッシュデータにはカテゴリ変数として6つの変数が含まれます。都道府県名 (`prefecture`)、発生場所を示す市区町村名(`cityName`) 、災害の種類 (`hazardType`) 、災害の種類の詳細 (`hazardType_sub`)、勾配 (`inclination`)、メッシュコード (`meshcode`)の6つです。ここではこれらのデータへの特徴量エンジニアリングを試みることにします。 - - + ## # A tibble: 8,476 x 14 + ## .prefecture administrative_… name_of_nearest… current_use + ## + ## 1 群馬県 10425 万座・鹿沢口 住宅,その他 + ## 2 群馬県 10426 長野原草津口 住宅 + ## 3 群馬県 10426 長野原草津口 店舗 + ## 4 群馬県 10424 羽根尾 住宅,その他 + ## 5 群馬県 10424 羽根尾 住宅 + ## 6 群馬県 10426 長野原草津口 住宅 + ## 7 群馬県 10424 群馬大津 住宅,店舗 + ## 8 群馬県 10382 下仁田 住宅 + ## 9 群馬県 10382 下仁田 住宅,店舗,事務所… + ## 10 群馬県 10382 下仁田 住宅 + ## # … with 8,466 more rows, and 10 more variables: usage_description , + ## # building_structure , + ## # proximity_with_transportation_facility , use_district , + ## # configuration , surrounding_present_usage , fire_area , + ## # urban_planning_area , forest_law , parks_law + +土砂災害・雪崩メッシュデータにはカテゴリ変数として6つの変数が含まれます。都道府県名 +(`prefecture`)、発生場所を示す市区町村名(`cityName`) 、災害の種類 +(`hazardType`) 、災害の種類の詳細 (`hazardType_sub`)、勾配 (`inclination`)、メッシュコード +(`meshcode`)の6つです。ここではこれらのデータへの特徴量エンジニアリングを試みることにします。 {:.input_area} ```R @@ -71,12 +81,30 @@ df_hazard %>% select_if(is.character) ``` - -私たちは都道府県名は最大で47の値があることを知っています。市町村名はそれより多いでしょう。しかし災害の種類 (`hazardType`) 、災害の種類の詳細 (`hazardType_sub`)、勾配 (`inclination`)にどのような値が含まれるかは把握できていません。カテゴリ変数の特徴量エンコーディングを実施する前に、どのような値が存在するか、その頻度と一緒に確認しておきましょう。 - -災害の種類をカウントします。災害の種類を記録する列として `hazardType`、`hazardType_sub`の2種類があります。がけ崩れ、地すべりでは災害種類の詳細を示す `hazardType_sub` が存在しないこともわかりました。また、2つの変数で頻度を集計すると、いくつかの組み合わせではわずかな頻度のものもあることがわかります。対してがけ崩れには `hazardType_sub` がなく、全体の7割近くを占めていることが判明しました。 - - + ## # A tibble: 4,315 x 6 + ## prefectureName cityName hazardType hazardType_sub inclination meshCode + ## + ## 1 沖縄県 名護市 がけ崩れ 3927771 + ## 2 沖縄県 豊見城市 がけ崩れ 3927252 + ## 3 沖縄県 豊見城市 がけ崩れ 3927251 + ## 4 沖縄県 那覇市 がけ崩れ 3927252 + ## 5 沖縄県 中頭郡中城村… がけ崩れ 3927263 + ## 6 沖縄県 浦添市 がけ崩れ 3927254 + ## 7 沖縄県 国頭郡本部町… がけ崩れ 3927774 + ## 8 沖縄県 糸満市 がけ崩れ 3927154 + ## 9 沖縄県 豊見城市 がけ崩れ 3927251 + ## 10 沖縄県 豊見城市 がけ崩れ 3927251 + ## # … with 4,305 more rows + +私たちは都道府県名は最大で47の値があることを知っています。市町村名はそれより多いでしょう。しかし災害の種類 (`hazardType`) +、災害の種類の詳細 (`hazardType_sub`)、勾配 +(`inclination`)にどのような値が含まれるかは把握できていません。カテゴリ変数の特徴量エンコーディングを実施する前に、どのような値が存在するか、その頻度と一緒に確認しておきましょう。 + +災害の種類をカウントします。災害の種類を記録する列として +`hazardType`、`hazardType_sub`の2種類があります。がけ崩れ、地すべりでは災害種類の詳細を示す +`hazardType_sub` +が存在しないこともわかりました。また、2つの変数で頻度を集計すると、いくつかの組み合わせではわずかな頻度のものもあることがわかります。対してがけ崩れには +`hazardType_sub` がなく、全体の7割近くを占めていることが判明しました。 {:.input_area} ```R @@ -85,40 +113,84 @@ df_hazard %>% arrange(hazardType) ``` + ## # A tibble: 17 x 3 + ## hazardType hazardType_sub n + ## + ## 1 がけ崩れ 2987 + ## 2 山林火災 計上なし 12 + ## 3 雪崩 全層 12 + ## 4 雪崩 表層 25 + ## 5 雪崩 14 + ## 6 地すべり 572 + ## 7 土石流 その他 16 + ## 8 土石流 河道内堆積物の泥流化 1 + ## 9 土石流 洪水 1 + ## 10 土石流 山腹崩壊 91 + ## 11 土石流 山林火災 54 + ## 12 土石流 施設災害 1 + ## 13 土石流 土砂流 88 + ## 14 土石流 噴火 1 + ## 15 土石流 民地盛土河川内流出 1 + ## 16 土石流 流木堆積 1 + ## 17 土石流 438 続いて勾配 (`inclination`)です。勾配は連続変数として数値化が可能なように見えます。このことは覚えておきましょう。 - - {:.input_area} ```R df_hazard %>% count(inclination) ``` + ## # A tibble: 129 x 2 + ## inclination n + ## + ## 1 0 1 + ## 2 1/0.1 1 + ## 3 1/0.2 1 + ## 4 1/1.35 1 + ## 5 1/10 28 + ## 6 1/11 4 + ## 7 1/12 10 + ## 8 1/13 2 + ## 9 1/14 2 + ## 10 1/15 3 + ## # … with 119 more rows ビールの支出データについては、カテゴリ変数は当日の日中の天候を記録した `weatherdaytime_06_00_18_00` のみです。 - - {:.input_area} ```R df_beer2018q2 %>% count(weatherdaytime_06_00_18_00) ``` + ## # A tibble: 33 x 2 + ## weatherdaytime_06_00_18_00 n + ## + ## 1 雨 2 + ## 2 雨一時曇 3 + ## 3 雨後一時曇 1 + ## 4 雨後時々曇 1 + ## 5 雨後曇 2 + ## 6 雨後曇一時晴 1 + ## 7 雨時々曇 4 + ## 8 快晴 2 + ## 9 晴 12 + ## 10 晴一時雨、雷を伴う 1 + ## # … with 23 more rows ## ダミー変数化 -カテゴリ変数を数値に変換する処理として最も一般的なのが、カテゴリ変数をダミー変数化してしまうことです。カテゴリに含まれる水準の値を特徴量に直接用いるもので、ダミーコーディング、One-Hotエンコーディング、effectコーディングの3種類があります。これらはカテゴリに含まれる$k$種類の値を特徴量として扱う際の挙動が異なります。 +カテゴリ変数を数値に変換する処理として最も一般的なのが、カテゴリ変数をダミー変数化してしまうことです。カテゴリに含まれる水準の値を特徴量に直接用いるもので、ダミーコーディング、One-Hotエンコーディング、effectコーディングの3種類があります。これらはカテゴリに含まれる\(k\)種類の値を特徴量として扱う際の挙動が異なります。 ### ダミーコーディング -ダミーコーディングは統計分析でも広く使われるカテゴリ変数の数値化手法です。該当する値を含む場合に1、そうでなければ0を各特徴量に与えます。ダミーコーディングではカテゴリが取りうる数、自由度 $k-1$の特徴量を生成します。自由度 $k-1$ で十分である理由は、他のダミー変数の値から残りの一つの値が推測可能だからです。 - -具体例で示しましょう。3つの水準 (A, B, C)をもつカテゴリ変数をダミーコーディングすると、2つの特徴量ができます。ここでは`feature_B`,`feature_C`という名前をつけました。ここで、Aをもつデータを探すのは簡単です。ダミー変数には0と1の値が格納され、該当しない場合には0ですので`feature_B`,`feature_C`両方で0のデータがAになります。Aのようなダミー変数に含まれないカテゴリは参照カテゴリと呼ばれます。参照カテゴリに対して、`feature_B`、`feature_C`の値が決まります。 - +ダミーコーディングは統計分析でも広く使われるカテゴリ変数の数値化手法です。該当する値を含む場合に1、そうでなければ0を各特徴量に与えます。ダミーコーディングではカテゴリが取りうる数、自由度 +\(k-1\)の特徴量を生成します。自由度 \(k-1\) で十分である理由は、他のダミー変数の値から残りの一つの値が推測可能だからです。 +具体例で示しましょう。3つの水準 (A, B, +C)をもつカテゴリ変数をダミーコーディングすると、2つの特徴量ができます。ここでは`feature_B`,`feature_C`という名前をつけました。ここで、Aをもつデータを探すのは簡単です。ダミー変数には0と1の値が格納され、該当しない場合には0ですので`feature_B`,`feature_C`両方で0のデータがAになります。Aのようなダミー変数に含まれないカテゴリは参照カテゴリと呼ばれます。参照カテゴリに対して、`feature_B`、`feature_C`の値が決まります。 {:.input_area} ```R @@ -129,18 +201,34 @@ df %>% juice() ``` - -ダミーコーディングを利用したモデリングはその結果の解釈が容易になります。これを地価公示データの都市計画区分 (`urban_planning_area`) をダミー変数化することで示しましょう。都市計画区分の列は次に示すように4つの値を取りますが、1つを参照カテゴリとして扱い、3つのダミー変数で表現することになります。 - - + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 8 x 2 + ## feature_B feature_C + ## + ## 1 0 0 + ## 2 0 0 + ## 3 0 0 + ## 4 0 0 + ## 5 1 0 + ## 6 1 0 + ## 7 0 1 + ## 8 0 1 + +ダミーコーディングを利用したモデリングはその結果の解釈が容易になります。これを地価公示データの都市計画区分 +(`urban_planning_area`) +をダミー変数化することで示しましょう。都市計画区分の列は次に示すように4つの値を取りますが、1つを参照カテゴリとして扱い、3つのダミー変数で表現することになります。 {:.input_area} ```R unique(df_lp_kanto$urban_planning_area) ``` - - + ## [1] "都計外" "非線引" "市街化" "調区" {:.input_area} ```R @@ -150,15 +238,36 @@ df_lp_kanto_dummy_baked <- step_dummy(urban_planning_area) %>% prep() %>% bake(posted_land_price, starts_with("urban_planning_area"), new_data = df_lp_kanto) +``` + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? +{:.input_area} +```R df_lp_kanto_dummy_baked ``` + ## # A tibble: 8,476 x 4 + ## posted_land_price urban_planning_ar… urban_planning_a… urban_planning_a… + ## + ## 1 4150 0 1 0 + ## 2 39000 0 0 1 + ## 3 56800 0 0 1 + ## 4 2700 0 0 1 + ## 5 12100 0 0 1 + ## 6 29800 0 0 1 + ## 7 16400 0 0 1 + ## 8 17400 0 0 1 + ## 9 25500 0 0 1 + ## 10 23300 0 0 1 + ## # … with 8,466 more rows 都市計画区分の情報のみを使って、公示価格を予測する線形回帰モデルを適用します。 - - {:.input_area} ```R df_lp_kanto_dummy_baked %>% @@ -166,11 +275,16 @@ df_lp_kanto_dummy_baked %>% tidy() ``` + ## # A tibble: 4 x 5 + ## term estimate std.error statistic p.value + ## + ## 1 (Intercept) 456795. 20131. 22.7 1.04e-110 + ## 2 urban_planning_area_調区 -432747. 90918. -4.76 1.97e- 6 + ## 3 urban_planning_area_都計外 -434220. 1246161. -0.348 7.28e- 1 + ## 4 urban_planning_area_非線引 -428760. 88608. -4.84 1.33e- 6 推定された結果の切片は、参照カテゴリの平均値を示します。つまり「市街化」の効果です。市街化に対して、他の係数はいずれも負値を取っています。この値は各カテゴリの平均値と切片(参照カテゴリ)との差を示します。これは市街化の影響が地下価格に影響し、他のカテゴリは効果が小さいことを示す結果です。ダミーエンコーディングではカテゴリの水準の一つを切片として利用可能なため、モデルの解釈が容易になるのです。 - - {:.input_area} ```R df_lp_kanto %>% @@ -178,7 +292,6 @@ df_lp_kanto %>% geom_bar(stat = "identity") ``` - ![](../images/lp_urban_planning_area_barplot-1.png) @@ -191,8 +304,6 @@ df_lp_kanto %>% 先と同じく、地価公示データの都市計画区分 (`urban_planning_area`) にOne-Hotエンコーディングを適用してみます。 - - {:.input_area} ```R df_lp_kanto %>% @@ -202,6 +313,26 @@ df_lp_kanto %>% bake(starts_with("urban_planning_area"), new_data = df_lp_kanto) ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 8,476 x 4 + ## urban_planning_ar… urban_planning_a… urban_planning_a… urban_planning_a… + ## + ## 1 0 0 1 0 + ## 2 0 0 0 1 + ## 3 0 0 0 1 + ## 4 0 0 0 1 + ## 5 0 0 0 1 + ## 6 0 0 0 1 + ## 7 0 0 0 1 + ## 8 0 0 0 1 + ## 9 0 0 0 1 + ## 10 0 0 0 1 + ## # … with 8,466 more rows 今回はカテゴリの水準数が4であったために4つの特徴量が新たに作られました。 @@ -209,8 +340,6 @@ df_lp_kanto %>% ダミーコーディングのように、参照カテゴリを利用するダミー変数化の方法としてeffectコーディングがあります。しかしeffectコーディングでは参照カテゴリは-1のベクトルで表現されます。 - - {:.input_area} ```R df %>% @@ -226,11 +355,26 @@ df %>% feature_C)) ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 8 x 4 + ## outcome n feature_B feature_C + ## + ## 1 1 4 -1 -1 + ## 2 0 4 -1 -1 + ## 3 0 4 -1 -1 + ## 4 0 4 -1 -1 + ## 5 1 2 1 0 + ## 6 0 2 1 0 + ## 7 1 2 0 1 + ## 8 1 2 0 1 effectコーディングの利点として、ダミーコーディングよりも結果の解釈が簡単ということがあります。ダミーコーディングを適用したデータ同様に、地価価格を予測する線形回帰モデルを実行してみましょう。 - - {:.input_area} ```R dummy_lm_res <- @@ -249,16 +393,24 @@ dummy_lm_res <- dummy_lm_res ``` + ## # A tibble: 4 x 5 + ## term estimate std.error statistic p.value + ## + ## 1 (Intercept) 132863. 313072. 0.424 0.671 + ## 2 urban_planning_area_調区 -108815. 319287. -0.341 0.733 + ## 3 urban_planning_area_都計外 -110288. 935024. -0.118 0.906 + ## 4 urban_planning_area_非線引 -104829. 318963. -0.329 0.742 -effectコーディングでは、切片はターゲットとなる変数の全体平均を表し、係数は各カテゴリの平均値と全体平均との差分を表します。このことにより、各カテゴリの効果 (main effect) を主眼に捉えることができます。なお参照カテゴリの係数を算出するにはカテゴリの係数を合計してマイナスをつけます。 - - +effectコーディングでは、切片はターゲットとなる変数の全体平均を表し、係数は各カテゴリの平均値と全体平均との差分を表します。このことにより、各カテゴリの効果 +(main effect) +を主眼に捉えることができます。なお参照カテゴリの係数を算出するにはカテゴリの係数を合計してマイナスをつけます。 {:.input_area} ```R -sum(dummy_lm_res$estimate[-1]) ``` + ## [1] 323931.8 ダミーエンコーディングの結果と同じく、参照カテゴリの「市街化」の効果が大きく、他のカテゴリによって平均が低くなっていることがわかりました。 @@ -266,7 +418,9 @@ effectコーディングでは、切片はターゲットとなる変数の全 これまでカテゴリ変数のデータに対して、ダミー変数と呼ばれる特徴量を生成する3つの方法を見てきました。ここでダミー変数化の欠点と解決策をあげておきましょう。 -ダミー変数化では、水準数が大きなカテゴリ変数に対してはうまく機能しないという弱点があります。ダミー変数のみを利用して作成されたモデルでは、入力に与えたデータのカテゴリが既存のカテゴリであれば問題ありません。しかし新しいカテゴリが与えられた際、モデルはこの情報を知らないために予測を出せなくなります。また、水準が多い場合には次元の数が増大することになります。時にデータ件数を上回る次元のデータを扱うことになるかもしれません。さらに、大量の0と一部の1を含んだスパースデータ (sparse data)になりやすいことに留意しましょう。スパースデータは計算コストが大きくなるという問題があります。カテゴリの数が多い場合には次の特徴量ハッシュやビンカウンティングが有効です。 +ダミー変数化では、水準数が大きなカテゴリ変数に対してはうまく機能しないという弱点があります。ダミー変数のみを利用して作成されたモデルでは、入力に与えたデータのカテゴリが既存のカテゴリであれば問題ありません。しかし新しいカテゴリが与えられた際、モデルはこの情報を知らないために予測を出せなくなります。また、水準が多い場合には次元の数が増大することになります。時にデータ件数を上回る次元のデータを扱うことになるかもしれません。さらに、大量の0と一部の1を含んだスパースデータ +(sparse +data)になりやすいことに留意しましょう。スパースデータは計算コストが大きくなるという問題があります。カテゴリの数が多い場合には次の特徴量ハッシュやビンカウンティングが有効です。 ## より多くのカテゴリに対応する @@ -280,15 +434,12 @@ effectコーディングでは、切片はターゲットとなる変数の全 具体例を紹介しましょう。土砂災害・雪崩メッシュデータのメッシュコードはユニークな数を集計すると2190になります。データ全体で1回しか出現しないメッシュコードも1380と多く、これをダミー変数化すると、データの分割方法によっては分散0の変数を含んでしまう可能性が高いです。 - - {:.input_area} ```R df_hazard$meshCode %>% n_distinct() ``` - - + ## [1] 2190 {:.input_area} ```R @@ -298,8 +449,7 @@ df_hazard %>% nrow() ``` - - + ## [1] 1380 {:.input_area} ```R @@ -309,13 +459,10 @@ df_hazard %>% geom_histogram(bins = 30) ``` - ![](../images/hazard_mesh_count-1.png) この出現頻度が低いメッシュコードをまとめてみましょう。今回は全体で10未満の出現頻度のメッシュコードを稀なカテゴリとして扱います。これにより、`meshCode`は28の水準を含むカテゴリになりました。この数が多いか少ないかは議論が残るところですが、ダミー変数化による分散ゼロの変数が含まれる確率は低くなったでしょう。 - - {:.input_area} ```R df_hazard %>% @@ -326,10 +473,30 @@ df_hazard %>% count(meshCode) ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 28 x 2 + ## meshCode n + ## + ## 1 4730243 21 + ## 2 4730341 16 + ## 3 4830732 10 + ## 4 4929652 18 + ## 5 5131043 21 + ## 6 5131044 14 + ## 7 5131122 15 + ## 8 5131141 13 + ## 9 5131142 15 + ## 10 5232354 10 + ## # … with 18 more rows ### 特徴量ハッシング -カテゴリの組み合わせる方法として、ハッシュ関数を用いることも可能です。ハッシュはある値のセットを関数を通して別の値(ハッシュ値)に格納する技術で、通常データベースや暗号化で使われますが、特徴量エンジニアリングでも有効です。ハッシュ関数では、潜在的に無限のパターンを取り得る値を有限の$m$種類の値に割り当てます。mはあらかじめ定義することができます。ハッシュ値はハッシュテーブルと呼ばれる場所に格納されますが、ハッシュテーブルのサイズは$m$です。入力値の範囲はハッシュ値が格納されるmよりも大きいので、異なる入力値が同じ出力値に割り当てられることもあります。これを衝突と呼びます。 +カテゴリの組み合わせる方法として、ハッシュ関数を用いることも可能です。ハッシュはある値のセットを関数を通して別の値(ハッシュ値)に格納する技術で、通常データベースや暗号化で使われますが、特徴量エンジニアリングでも有効です。ハッシュ関数では、潜在的に無限のパターンを取り得る値を有限の\(m\)種類の値に割り当てます。mはあらかじめ定義することができます。ハッシュ値はハッシュテーブルと呼ばれる場所に格納されますが、ハッシュテーブルのサイズは\(m\)です。入力値の範囲はハッシュ値が格納されるmよりも大きいので、異なる入力値が同じ出力値に割り当てられることもあります。これを衝突と呼びます。 衝突が発生した際には情報が失われることに注意が必要です。 @@ -351,8 +518,6 @@ T.B.D. カウント変数に含まれる各水準の頻度を求めたものがカウントエンコーディングです。すべての水準がデータ中で一度は出現ため、値は1以上の整数値になります。土砂災害・雪崩メッシュデータに対してカウントエンコーディングを行った結果を次に示します。 - - {:.input_area} ```R df_hazard %>% @@ -364,15 +529,22 @@ df_hazard %>% ungroup() ``` + ## # A tibble: 5 x 3 + ## hazardDate hazardType hazardType_n + ## + ## 1 2006-06-12 がけ崩れ 2987 + ## 2 2009-06-17 山林火災 12 + ## 3 2006-04-09 雪崩 51 + ## 4 2006-06-10 地すべり 572 + ## 5 2007-06-18 土石流 693 カウントエンコーディングは直感的で実装も簡単ですが、 カテゴリ内での出現頻度が多ければ多いほど、特徴量の値は大きくなり、影響も強くなります。しかしもっとも大きい値(あるいは小さい値)が二番目の値と大きな差があってもその差は縮小されて表現されます。一方で、元は異なる水準であったものが同じ頻度で出現する場合にはエンコード後の値が同じになってしまうことに注意です。 #### ラベルエンコーディング -ラベルエンコーディング (label encoding, ordinal encoder) はカテゴリに対して一意の数値を割り振るというものです。アイデアは単純ですが、多くの場合これでカテゴリがもつ特徴を拾い上げることはできずに利用する場面は限定的でしょう。 - - +ラベルエンコーディング (label encoding, ordinal encoder) +はカテゴリに対して一意の数値を割り振るというものです。アイデアは単純ですが、多くの場合これでカテゴリがもつ特徴を拾い上げることはできずに利用する場面は限定的でしょう。 {:.input_area} ```R @@ -383,12 +555,21 @@ df_hazard %>% head(10) ``` + ## # A tibble: 5 x 2 + ## hazardType hazardType_num + ## + ## 1 がけ崩れ 1 + ## 2 地すべり 4 + ## 3 土石流 5 + ## 4 山林火災 2 + ## 5 雪崩 3 #### ターゲットエンコーディング -ターゲットエンコーディング (target-based encoding, likelihood encoding) は、カテゴリ変数と対応する目的変数の値を利用した方法です。カテゴリ変数の水準ごとに、水準の項目を目的変数の平均値に置き換えるという処理を行います。例えば、カテゴリ変数にAという項目が4つ含まれ、それぞれに1.5, 3.0, 0, 1.2のoutcomeが与えられているとします。この場合、outcomeの平均値は1.425なので、カテゴリ変数のAは1.425に置き換えられます。 - - +ターゲットエンコーディング (target-based encoding, likelihood encoding) +は、カテゴリ変数と対応する目的変数の値を利用した方法です。カテゴリ変数の水準ごとに、水準の項目を目的変数の平均値に置き換えるという処理を行います。例えば、カテゴリ変数にAという項目が4つ含まれ、それぞれに1.5, +3.0, 0, +1.2のoutcomeが与えられているとします。この場合、outcomeの平均値は1.425なので、カテゴリ変数のAは1.425に置き換えられます。 {:.input_area} ```R @@ -398,27 +579,58 @@ tibble( catto_mean(response = outcome) ``` + ## # A tibble: 4 x 2 + ## feature outcome + ## + ## 1 1.42 1.5 + ## 2 1.42 3 + ## 3 1.42 0 + ## 4 1.42 1.2 また以下のように目的変数が論理値である場合には、それを数値に変換した値を利用します(RではTRUEが1、FALSEが0です)。 - - {:.input_area} ```R df +``` + + ## # A tibble: 8 x 3 + ## feature outcome n + ## + ## 1 A 1 4 + ## 2 A 0 4 + ## 3 A 0 4 + ## 4 A 0 4 + ## 5 B 1 2 + ## 6 B 0 2 + ## 7 C 1 2 + ## 8 C 1 2 +{:.input_area} +```R df %>% catto_mean(response = outcome) ``` + ## # A tibble: 8 x 3 + ## feature outcome n + ## + ## 1 0.25 1 4 + ## 2 0.25 0 4 + ## 3 0.25 0 4 + ## 4 0.25 0 4 + ## 5 0.5 1 2 + ## 6 0.5 0 2 + ## 7 1 1 2 + ## 8 1 1 2 ターゲットエンコーディングでは、カテゴリの水準ごとにデータ全体の値を参照することになるので直接利用する場合はデータ漏洩に繋がります。また頻度の低い水準がある場合も過学習の原因になってしまう可能性があることに気をつけましょう。 ##### Leave one out エンコーディング -ターゲットエンコーディングではデータ漏洩の問題が指摘されました。これを防ぐ方法としてLeave one out エンコーディングがあります(完全にデータ漏洩を防げるわけではありません)。Leave one out エンコーディングではターゲットエンコーディング同様の計算を行いますが、自身を除いて計算されるのが特徴です。 - - +ターゲットエンコーディングではデータ漏洩の問題が指摘されました。これを防ぐ方法としてLeave one out +エンコーディングがあります(完全にデータ漏洩を防げるわけではありません)。Leave +one out エンコーディングではターゲットエンコーディング同様の計算を行いますが、自身を除いて計算されるのが特徴です。 {:.input_area} ```R @@ -427,8 +639,20 @@ df %>% catto_loo(response = outcome) ``` - -`feature` A のうち、`outcome` が1なのは最初の行のみで他の3つの行は0です。そのため最初のAではすべて0を参照することになり、他の行では、0が1つ、1が2つという状況で計算が行われます。 + ## # A tibble: 8 x 3 + ## feature outcome n + ## + ## 1 0 1 4 + ## 2 0.333 0 4 + ## 3 0.333 0 4 + ## 4 0.333 0 4 + ## 5 0 1 2 + ## 6 1 0 2 + ## 7 1 1 2 + ## 8 1 1 2 + +`feature` A のうち、`outcome` +が1なのは最初の行のみで他の3つの行は0です。そのため最初のAではすべて0を参照することになり、他の行では、0が1つ、1が2つという状況で計算が行われます。 ## カテゴリ変数の縮約・拡張 @@ -444,9 +668,7 @@ XOR 一つの特徴量から複数の特徴量を生成します。カテゴリ変数の値の分割がこれに当たります。テキストから必要な情報の取得する、空白があるとデータを区切る、など処理の方法はさまざまです。 -ビールの支出データに含まれるweatherdaytime_06_00_18_00には、「晴」や「曇」だけでなく「曇一時雨」や「雨後時々曇」といった気象に関する項目が含まれます。項目の組み合わせによる表現が可能であるため、カテゴリの数は多くなっています。 - - +ビールの支出データに含まれるweatherdaytime\_06\_00\_18\_00には、「晴」や「曇」だけでなく「曇一時雨」や「雨後時々曇」といった気象に関する項目が含まれます。項目の組み合わせによる表現が可能であるため、カテゴリの数は多くなっています。 {:.input_area} ```R @@ -454,13 +676,25 @@ df_beer2018q2 %>% count(weatherdaytime_06_00_18_00) ``` + ## # A tibble: 33 x 2 + ## weatherdaytime_06_00_18_00 n + ## + ## 1 雨 2 + ## 2 雨一時曇 3 + ## 3 雨後一時曇 1 + ## 4 雨後時々曇 1 + ## 5 雨後曇 2 + ## 6 雨後曇一時晴 1 + ## 7 雨時々曇 4 + ## 8 快晴 2 + ## 9 晴 12 + ## 10 晴一時雨、雷を伴う 1 + ## # … with 23 more rows この一次や時々によって区切ることが可能な項目 を新たな特徴量として活用するのがexpansion encodingになります。 - - {:.input_area} ```R df_beer2018q2_baked <- @@ -471,15 +705,37 @@ df_beer2018q2_baked <- into = paste("weatherdaytime_06_00_18_00", c("main", "sub"), sep = "_")) +``` + + ## Warning: Expected 2 pieces. Additional pieces discarded in 6 rows [9, 12, + ## 42, 62, 65, 79]. + ## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 30 rows + ## [1, 2, 3, 7, 8, 11, 13, 15, 16, 18, 20, 25, 32, 34, 36, 39, 43, 46, 47, + ## 48, ...]. + +{:.input_area} +```R df_beer2018q2_baked ``` + ## # A tibble: 92 x 4 + ## date expense weatherdaytime_06_00_18_00… weatherdaytime_06_00_18_… + ## + ## 1 2018-07-01 58.0 晴 + ## 2 2018-07-02 42.8 快晴 + ## 3 2018-07-03 23.9 薄曇 + ## 4 2018-07-04 28.6 曇 雨 + ## 5 2018-07-05 35.4 雨 曇 + ## 6 2018-07-06 29.1 雨 曇 + ## 7 2018-07-07 53.9 曇 + ## 8 2018-07-08 61.6 薄曇 + ## 9 2018-07-09 38.4 晴 曇 + ## 10 2018-07-10 33.1 晴 薄曇 + ## # … with 82 more rows 複雑なカテゴリを評価するのではなく、大雑把なカテゴリとして扱いたい場合にはカテゴリの項目を減らすことが有効でしょう。 - - {:.input_area} ```R df_beer2018q2_baked %>% @@ -487,8 +743,25 @@ df_beer2018q2_baked %>% weatherdaytime_06_00_18_00_sub) ``` - -Expansion encodingを実行した時は、不用意に分散ゼロの特徴量をつくっていないか確認しておきましょう。例えばある学校の複数のクラスで学校名とクラス名が記録され、これを分割する時は、共通して含まれる「中学校」は不要となります。 + ## # A tibble: 13 x 3 + ## weatherdaytime_06_00_18_00_main weatherdaytime_06_00_18_00_sub n + ## + ## 1 雨 曇 12 + ## 2 雨 2 + ## 3 快晴 2 + ## 4 晴 雨、雷を伴う 1 + ## 5 晴 曇 10 + ## 6 晴 薄曇 9 + ## 7 晴 12 + ## 8 大雨 曇 1 + ## 9 曇 雨 11 + ## 10 曇 晴 11 + ## 11 曇 7 + ## 12 薄曇 晴 7 + ## 13 薄曇 7 + +Expansion +encodingを実行した時は、不用意に分散ゼロの特徴量をつくっていないか確認しておきましょう。例えばある学校の複数のクラスで学校名とクラス名が記録され、これを分割する時は、共通して含まれる「中学校」は不要となります。 ## 順序付きデータのエンコーディング @@ -508,10 +781,7 @@ Expansion encodingを実行した時は、不用意に分散ゼロの特徴量 中 = 0, 大 = 0.71, 小=-0.71 -2次 -中 = 0.41, 大 = -0.82, 小=0.41 - - +2次 中 = 0.41, 大 = -0.82, 小=0.41 {:.input_area} ```R @@ -544,19 +814,25 @@ lp_test_dummy <- bake(mod_rec_trained, new_data = df_test) names(lp_test_dummy) # 1住居がない(一番多い) ``` - ## まとめ -- カテゴリ変数はツリーベースのモデルを除いて、モデルに適用可能な状態、数値に変換する必要がある -- もっとも単純なものはカテゴリに含まれる値を独立した変数として扱うこと - - カテゴリ内の順序を考慮するには別な方法が必要 -- テキストも同様に数値化が必要。一般的には頻度の少ない単語が除外される + - カテゴリ変数はツリーベースのモデルを除いて、モデルに適用可能な状態、数値に変換する必要がある + - もっとも単純なものはカテゴリに含まれる値を独立した変数として扱うこと + - カテゴリ内の順序を考慮するには別な方法が必要 + - テキストも同様に数値化が必要。一般的には頻度の少ない単語が除外される ## 関連項目 -- [次元削減](../03/dimension-reduction) + - [次元削減](../03/dimension-reduction) ## 参考文献 -- Max Kuhn and Kjell Johnson (2013). Applied Predictive Modeling (Springer) -- Max Kuhn and Kjell Johnson (2019). Feature Engineering and Selection: A Practical Approach for Predictive Models (CRC Press) + - Max Kuhn and Kjell Johnson (2013). Applied Predictive Modeling + (Springer) + - Max Kuhn and Kjell Johnson (2019). Feature Engineering and + Selection: A Practical Approach for Predictive Models (CRC + Press) + + + +1. カテゴリを数値に変換する処理は全般的にエンコーディングと呼ばれます。これは、カテゴリが持つ情報をエンコードして数値に変換する作業を踏襲した命名です。 diff --git a/_build/02/date-and-time.md b/_build/02/date-and-time.md index c7d220d..97c35ed 100644 --- a/_build/02/date-and-time.md +++ b/_build/02/date-and-time.md @@ -9,25 +9,16 @@ prev_page: next_page: url: /02/spatial-data title: '地理空間データの取り扱い' +output: github_document + comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" --- - - -{:.input_area} -```R -source(here::here("R/setup.R")) -``` - - - - {:.input_area} ```R library(lubridate) ``` - # 日付・時間データの取り扱い 日付や時間の要素をもつデータは、 @@ -38,19 +29,20 @@ library(lubridate) 日付・時間データに対する特徴量エンジニアリングの基本方針は以下の3点です。 -- 日付・時間データの特徴量... 日付・時間データ自身を構成する要素から特徴量を生成します -- ラグ特徴量... 特定の時間をもとに、そこからの経過時間を扱います -- 窓枠特徴量... 固定された期間に含まれる値を要約します + - 日付・時間データの特徴量… 日付・時間データ自身を構成する要素から特徴量を生成します + - ラグ特徴量… 特定の時間をもとに、そこからの経過時間を扱います + - 窓枠特徴量… +固定された期間に含まれる値を要約します ここでは、日付・時間データが記録されているビールへの支出データを利用します。具体的な特徴量エンジニアリングの手法を紹介する前に、ビールの売り上げに対して、日付・時間データの何が効いているか、また、どのような状況が想定されるか仮説を立ててみましょう。思いつくものとして次の要因があります。 -- 平日・休日 - -より厳密には週末かそうでないか… 土日は効果がありそうだ - - 連休の数... 長すぎると売り上げが伸びなくなるのではないか -- 夏休みにはどのような挙動を示すだろうか -- 関係しそうではないもの - - 月初や月末という効果は売り上げには貢献しないだろう(週ごとの影響はない) - + - 平日・休日 -より厳密には週末かそうでないか… 土日は効果がありそうだ + - 連休の数… +長すぎると売り上げが伸びなくなるのではないか + - 夏休みにはどのような挙動を示すだろうか + - 関係しそうではないもの + - 月初や月末という効果は売り上げには貢献しないだろう(週ごとの影響はない) + このような仮説を検証できるよう、日付・時間に関する特徴量を生成していきます。 ## 日付・時間データ自体を特徴量とする @@ -61,16 +53,11 @@ library(lubridate) 日付や時間のデータは複数の要素で構成されます。例えば「2019年7月10日」であれば、年月日をそれぞれ分けて2019、7、10に分解可能です。これに時間が加わればさらに時分秒の要素に分解することもできます。 - - {:.input_area} ```R mod_fml <- formula(expense ~ date + weatherdaytime_06_00_18_00) ``` - - - {:.input_area} ```R df_baked_split_date <- @@ -79,10 +66,27 @@ df_baked_split_date <- step_date(date) %>% prep() %>% juice() +``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + +{:.input_area} +```R glimpse(df_baked_split_date) ``` + ## Observations: 92 + ## Variables: 6 + ## $ date 2018-07-01, 2018-07-02, 2018-07-03,… + ## $ weatherdaytime_06_00_18_00 晴, 快晴, 薄曇, 曇一時雨, 雨後一時曇, 雨後時々曇, 曇, 薄曇… + ## $ expense 58.02, 42.79, 23.89, 28.60, 35.44, 2… + ## $ date_dow 日, 月, 火, 水, 木, 金, 土, 日, 月, 火, 水, 木, … + ## $ date_month 7, 7, 7, 7, 7, 7, 7, 7, 7, … + ## $ date_year 2018, 2018, 2018, 2018, 2018, 2018, … 日付もそれほど重要ではなさそうです。むしろ日付によって変わる平日・休日の違いが影響しそうですが、これについてはあとで処理を加えていくことにします。 @@ -94,14 +98,12 @@ glimpse(df_baked_split_date) ビールの支出データに記録されている日付の曜日をダミーコーディングする例を考えてみましょう。曜日は7つの値を取りますが、コントラスト関数は6つのダミー変数で曜日を表現することになります。 -6列... 該当する曜日で1, そうでない場合に0 +6列… 該当する曜日で1, そうでない場合に0 まずは年月日からなる日付の変数から曜日を取り出す必要があります。 日付を記録するdate列の要素が分解され、新たな特徴量として追加されました。それでは曜日のダミーコーディングを実行します。 - - {:.input_area} ```R df_baked_split_date %>% @@ -112,8 +114,28 @@ df_baked_split_date %>% select(starts_with("date_dow"), everything()) ``` - - + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 92 x 11 + ## date_dow_月 date_dow_火 date_dow_水 date_dow_木 date_dow_金 date_dow_土 + ## + ## 1 0 0 0 0 0 0 + ## 2 1 0 0 0 0 0 + ## 3 0 1 0 0 0 0 + ## 4 0 0 1 0 0 0 + ## 5 0 0 0 1 0 0 + ## 6 0 0 0 0 1 0 + ## 7 0 0 0 0 0 1 + ## 8 0 0 0 0 0 0 + ## 9 1 0 0 0 0 0 + ## 10 0 1 0 0 0 0 + ## # … with 82 more rows, and 5 more variables: date , + ## # weatherdaytime_06_00_18_00 , expense , date_month , + ## # date_year {:.input_area} ```R @@ -126,25 +148,33 @@ df_beer_prep <- rep_split_date %>% prep(training = df_beer2018q2) %>% juice(expense, starts_with("date"), temperature_average) - -df_beer_prep ``` - - + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? {:.input_area} ```R -df_baked_split_date %>% - recipe(expense ~ .) %>% - step_dummy(date_dow, one_hot = TRUE) %>% - prep(training = df_baked_split_date) %>% - bake(new_data = df_baked_split_date) %>% - select(starts_with("date_dow"), everything()) +df_beer_prep ``` - - + ## # A tibble: 92 x 6 + ## date temperature_average expense date_dow date_month date_year + ## + ## 1 2018-07-01 28.1 58.0 日 " 7" 2018 + ## 2 2018-07-02 28.8 42.8 月 " 7" 2018 + ## 3 2018-07-03 28.4 23.9 火 " 7" 2018 + ## 4 2018-07-04 27.6 28.6 水 " 7" 2018 + ## 5 2018-07-05 26.7 35.4 木 " 7" 2018 + ## 6 2018-07-06 21.7 29.1 金 " 7" 2018 + ## 7 2018-07-07 25 53.9 土 " 7" 2018 + ## 8 2018-07-08 27.4 61.6 日 " 7" 2018 + ## 9 2018-07-09 27.3 38.4 月 " 7" 2018 + ## 10 2018-07-10 28.5 33.1 火 " 7" 2018 + ## # … with 82 more rows {:.input_area} ```R @@ -152,6 +182,22 @@ df_baked_split_date %>% stringr::str_subset(timeDate::listHolidays(), "^JP") ``` + ## [1] "JPAutumnalEquinox" "JPBankHolidayDec31" + ## [3] "JPBankHolidayJan2" "JPBankHolidayJan3" + ## [5] "JPBunkaNoHi" "JPChildrensDay" + ## [7] "JPComingOfAgeDay" "JPConstitutionDay" + ## [9] "JPEmperorsBirthday" "JPGantan" + ## [11] "JPGreeneryDay" "JPHealthandSportsDay" + ## [13] "JPKeirouNOhi" "JPKenkokuKinenNoHi" + ## [15] "JPKenpouKinenBi" "JPKinrouKanshaNoHi" + ## [17] "JPKodomoNoHi" "JPKokuminNoKyujitu" + ## [19] "JPMarineDay" "JPMidoriNoHi" + ## [21] "JPNatFoundationDay" "JPNationalCultureDay" + ## [23] "JPNationHoliday" "JPNewYearsDay" + ## [25] "JPRespectForTheAgedDay" "JPSeijinNoHi" + ## [27] "JPShuubunNoHi" "JPTaiikuNoHi" + ## [29] "JPTennouTanjyouBi" "JPThanksgivingDay" + ## [31] "JPUmiNoHi" ## カレンダー(祝日) @@ -161,8 +207,6 @@ stringr::str_subset(timeDate::listHolidays(), "^JP") 今度は分解した曜日の情報をもとに、ビールの売り上げは「翌日に仕事が控えている曜日よりも休日の方が増えそうだ」という直感を調べてみましょう。 - - {:.input_area} ```R df_baked_split_date <- @@ -172,9 +216,6 @@ df_baked_split_date <- FALSE)) ``` - - - {:.input_area} ```R df_baked_split_date %>% @@ -186,20 +227,20 @@ df_baked_split_date %>% facet_wrap(~ date_month) ``` +![](../images/beer_holiday_effect_comperad-1.png) 目論見通り、どの月でも平日よりも休日の方が支出が増えているようです。また、8月は週ごとに変動が大きく、9月では平日との差がほとんどないということもグラフから読み取れます。新たな特徴量の作成と関係の図示により、経験的な推論を確認するだけでなく、モデルに対する新たな洞察も得ることができました。 7月から9月にかけては -- 海の日(7月第3月曜日) -- 山の日 (8月11日) ... 2016年に設立。 -- 敬老の日 (9月第3月曜日) -- 秋分の日 (秋分日... ) + - 海の日(7月第3月曜日) + - 山の日 (8月11日) … 2016年に設立。 + - 敬老の日 (9月第3月曜日) + - 秋分の日 (秋分日… +) が祝日となります。また、この期間で考慮すべき日付として「お盆」の期間があります。多くの企業でこの期間は夏休みとなっていると考えられます。8月13日から15日のデータも他の日付と区別できるようにしておきます。 - - {:.input_area} ```R df_beer_prep <- @@ -213,15 +254,38 @@ df_beer_prep <- # timeDate::listHolidays() が山の日に未対応なのでフラグを作る処理を用意する mutate(date_JPYamaNoHi = as.numeric(date == ymd("2018-08-11")), is_obon = between(date, ymd("2018-08-13"), ymd("2018-08-15"))) +``` + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? +{:.input_area} +```R df_beer_prep ``` + ## # A tibble: 92 x 11 + ## date temperature_ave… expense date_dow date_month date_year + ## + ## 1 2018-07-01 28.1 58.0 日 " 7" 2018 + ## 2 2018-07-02 28.8 42.8 月 " 7" 2018 + ## 3 2018-07-03 28.4 23.9 火 " 7" 2018 + ## 4 2018-07-04 27.6 28.6 水 " 7" 2018 + ## 5 2018-07-05 26.7 35.4 木 " 7" 2018 + ## 6 2018-07-06 21.7 29.1 金 " 7" 2018 + ## 7 2018-07-07 25 53.9 土 " 7" 2018 + ## 8 2018-07-08 27.4 61.6 日 " 7" 2018 + ## 9 2018-07-09 27.3 38.4 月 " 7" 2018 + ## 10 2018-07-10 28.5 33.1 火 " 7" 2018 + ## # … with 82 more rows, and 5 more variables: date_JPKeirouNOhi , + ## # date_JPShuubunNoHi , date_JPUmiNoHi , date_JPYamaNoHi , + ## # is_obon 平日であれば1を与える列を追加します。 - - {:.input_area} ```R df_beer_prep <- @@ -239,8 +303,20 @@ df_beer_prep <- df_beer_prep ``` - - + ## # A tibble: 92 x 4 + ## expense is_weekday date_month temperature_average + ## + ## 1 58.0 0 " 7" 28.1 + ## 2 42.8 1 " 7" 28.8 + ## 3 23.9 1 " 7" 28.4 + ## 4 28.6 1 " 7" 27.6 + ## 5 35.4 1 " 7" 26.7 + ## 6 29.1 1 " 7" 21.7 + ## 7 53.9 0 " 7" 25 + ## 8 61.6 0 " 7" 27.4 + ## 9 38.4 1 " 7" 27.3 + ## 10 33.1 1 " 7" 28.5 + ## # … with 82 more rows {:.input_area} ```R @@ -252,14 +328,18 @@ df_beer_baked <- juice() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? ## シークエンス 時系列データのように、変動が大きなデータでは全体の傾向を掴むのが困難な時があります。そのような場合、移動平均などの平滑化を利用することでデータを「均し」、傾向をつかみやすくなることがあります。これらの平滑化処理は時系列データに含まれる過剰なノイズを減らすのに役立ちます。 -移動平均は対象時点のデータとその前後の値を足して平均を取ることで計算されます。前後の値をいくつ取るか、これは窓枠の数 (ウィンドウ window size)と呼ばれ、あらかじめ決める必要があります。ウィンドウの数は重要で、大きすぎると平滑化により非線形の関係が消えてしまう恐れがあります。例えばウィンドウを3とした時は自身と前後の一つずつの値から値が求まります。この場合では同じ値が使われることは少ないですが、大きなウィンドウを設定すると別々のデータで同じデータが使われることになるため、個別のデータがより平均に近づきます。 - - +移動平均は対象時点のデータとその前後の値を足して平均を取ることで計算されます。前後の値をいくつ取るか、これは窓枠の数 (ウィンドウ window +size)と呼ばれ、あらかじめ決める必要があります。ウィンドウの数は重要で、大きすぎると平滑化により非線形の関係が消えてしまう恐れがあります。例えばウィンドウを3とした時は自身と前後の一つずつの値から値が求まります。この場合では同じ値が使われることは少ないですが、大きなウィンドウを設定すると別々のデータで同じデータが使われることになるため、個別のデータがより平均に近づきます。 {:.input_area} ```R @@ -278,19 +358,20 @@ p2 <- df_beer2018q2 %>% plot_grid(p1, p2, ncol = 1) ``` +![](../images/beer_ts_window-1.png) 短期間の中央値を利用することも(異常値がある場合) 異常値を含むように窓枠の数を決める -- 移動平均 -- 外れ値をもつ場合には平滑化が有効 + - 移動平均 + - 外れ値をもつ場合には平滑化が有効 ## 季節成分・周期成分 時間スケール -- 一日、一週間、一ヶ月、四半期、一年 + - 一日、一週間、一ヶ月、四半期、一年 周期性がある @@ -298,8 +379,6 @@ plot_grid(p1, p2, ncol = 1) sin, cos変換 - - {:.input_area} ```R set.seed(313) @@ -317,17 +396,13 @@ cos_obj <- prep(cos_trans, training = examples) transformed_te <- bake(cos_obj, examples) plot(examples$V1, transformed_te$V1) - -plot(sin(1:12), cos(1:12)) ``` +![](../images/step_hyperbolic-1.png) ## 特定の時点からの経過 -期間内のある瞬間の情報 -特定のイベントからの経過時間 - - +期間内のある瞬間の情報 特定のイベントからの経過時間 {:.input_area} ```R @@ -345,6 +420,25 @@ recipe(~ ., data = df) %>% bake(df) ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 10 x 7 + ## x index day lag_2_index lag_2_day lag_3_index lag_3_day + ## + ## 1 0.245 1 1999-01-01 NA NA NA NA + ## 2 0.799 2 1999-01-02 NA NA NA NA + ## 3 0.208 3 1999-01-03 1 1999-01-01 NA NA + ## 4 0.0207 4 1999-01-04 2 1999-01-02 1 1999-01-01 + ## 5 0.430 5 1999-01-05 3 1999-01-03 2 1999-01-02 + ## 6 0.900 6 1999-01-06 4 1999-01-04 3 1999-01-03 + ## 7 0.934 7 1999-01-07 5 1999-01-05 4 1999-01-04 + ## 8 0.450 8 1999-01-08 6 1999-01-06 5 1999-01-05 + ## 9 0.237 9 1999-01-09 7 1999-01-07 6 1999-01-06 + ## 10 0.171 10 1999-01-10 8 1999-01-08 7 1999-01-07 時間要素のうち、何周目か @@ -352,7 +446,7 @@ recipe(~ ., data = df) %>% ラグ特徴量 -重み付け... 遠くなるものへは重みを小さくする +重み付け… 遠くなるものへは重みを小さくする @@ -360,7 +454,6 @@ recipe(~ ., data = df) %>% ## 関連項目 -- [データ分割](../03/data-splitting) + - [データ分割](../03/data-splitting) ## 参考文献 - diff --git a/_build/02/numeric.md b/_build/02/numeric.md index 5cb0609..fb5c0a5 100644 --- a/_build/02/numeric.md +++ b/_build/02/numeric.md @@ -9,17 +9,11 @@ prev_page: next_page: url: /02/categorical title: 'カテゴリデータの取り扱い' +output: github_document + comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" --- - - -{:.input_area} -```R -source(here::here("R/setup.R")) -``` - - # 数値データの取り扱い データ分析においてもっとも一般的な型が数値データです。商品の価格やウェブページのアクセス件数、株価の変動など多くのデータが数値で表されます。このことからわかるように、一言で数値データといっても実数から整数、マイナスの値を持つものまで多様な種類が存在します。 @@ -34,11 +28,11 @@ source(here::here("R/setup.R")) 数値データはありふれたデータ形式ですが、その反面数多くの問題を抱えていることがあります。その特徴をあげてみます。 -- スケールが大きく異なる -- 歪んだ分布をもつ -- 大小の外れ値を含む -- 変数間で、線形では表現できないような複雑な関係を持っている -- 冗長な情報 + - スケールが大きく異なる + - 歪んだ分布をもつ + - 大小の外れ値を含む + - 変数間で、線形では表現できないような複雑な関係を持っている + - 冗長な情報 これらの問題は、回帰か分類かという課題設定に応じて、適用するモデルの種類によって顕在化します。しかし適切なモデルを選択することで問題のいくつかを軽減できる見込みもあります。例を見てみましょう。 @@ -46,9 +40,9 @@ k近傍法やサポートベクターマシンは、特徴空間上の外れ値 数値データに対する特徴量エンジニアリングの作戦は次の3種類に分けて考えられます。 -- 個々の数値変数の問題について直接対処する -- 変数変換 -- 変数間の関係を見つめ直す + - 個々の数値変数の問題について直接対処する + - 変数変換 + - 変数間の関係を見つめ直す この章では、これらの作戦を個別に紹介してきます。 @@ -56,9 +50,8 @@ k近傍法やサポートベクターマシンは、特徴空間上の外れ値 数値データの特徴量エンジニアリングとして最初に検討するのが対数変換です。これは[前処理](../01/tidy-data)で扱ったスケール変換と、対象の変数の値を変えるという意味では同じですが、大きな違いがあります。スケール変換では変数のばらつきを元にする変換のために変換後の値でも分布は変わりません。しかし対数変換ではデータの分布が変化します。 -対数変換の効果が大きいのは偏りがある数値データです。裾の長いデータ (long tail) とも呼びます。次の図は地価公示データの地価をヒストグラムにプロットした図と、同じデータに対数変換を施したものです。 - - +対数変換の効果が大きいのは偏りがある数値データです。裾の長いデータ (long tail) +とも呼びます。次の図は地価公示データの地価をヒストグラムにプロットした図と、同じデータに対数変換を施したものです。 {:.input_area} ```R @@ -82,7 +75,6 @@ plot_grid( ncol = 2) ``` - ![](../images/log_transform_compared-1.png) 対数変換には次の特徴があります。特徴量のスケールが大きい時はその範囲を縮小し、小さい時は拡大します。これにより、裾の長い分布を押しつぶしたように山のある分布に近づけることができます。また分散が大きなデータでは平均値が大きいほど等分散となりやすい傾向にあります。 @@ -91,23 +83,45 @@ plot_grid( 正規分布に従わないデータに対して線形回帰モデルを適用すると推定が思うように行かないことがあります。これは線形回帰モデルが一般に入力に対して正規分布のデータを仮定しているためです。このようなデータでは、正規分布でない分布を背景とする回帰モデルを利用することが一番の対策です。しかしここではデータの分布を変えることでデータを正規分布に近づける方法を紹介しましょう。 -ポアソン分布は平均と分散が等しいという性質があります。平均 $\lambda$ が大きいほど分散が大きくなり、分布の裾が重くなります。地価公示データの土地の面積を示す変数 `acreage` は裾の重い分布をしてます (ヒストグラムは後ほど作成します)。 このデータを正規分布に近づけるにはどうすれば良いでしょう。先に紹介した対数変換は1つの手段です。 - -データが正規分布に従っているかは、シャピロ-ウィルクの正規性検定や正規確率グラフ (QQプロット) の描画で確かめられます。 を例に見てみます。元のデータと対数変換した値を使って、シャピロ-ウィルクの正規性検定を行います。この統計手法の帰無仮説は「変数は正規分布にしたがう」です。 $P < 0.05$ となれば帰無仮説は棄却されます。すなわち正規分布の仮定が保証できなくなります。 - +ポアソン分布は平均と分散が等しいという性質があります。平均 \(\lambda\) +が大きいほど分散が大きくなり、分布の裾が重くなります。地価公示データの土地の面積を示す変数 +`acreage` は裾の重い分布をしてます (ヒストグラムは後ほど作成します)。 +このデータを正規分布に近づけるにはどうすれば良いでしょう。先に紹介した対数変換は1つの手段です。 +データが正規分布に従っているかは、シャピロ-ウィルクの正規性検定や正規確率グラフ (QQプロット) の描画で確かめられます。 +を例に見てみます。元のデータと対数変換した値を使って、シャピロ-ウィルクの正規性検定を行います。この統計手法の帰無仮説は「変数は正規分布にしたがう」です。 +\(P < 0.05\) となれば帰無仮説は棄却されます。すなわち正規分布の仮定が保証できなくなります。 {:.input_area} ```R shapiro.test(sample(df_lp_kanto$acreage, 5000)) %>% tidy() +``` + + ## # A tibble: 1 x 3 + ## statistic p.value method + ## + ## 1 0.0347 5.12e-95 Shapiro-Wilk normality test + +```R shapiro.test(log(sample(df_lp_kanto$acreage, 5000))) %>% tidy() -shapiro.test(log(sample(df_lp_kanto$acreage, 5000), base = 10)) %>% tidy() ``` + ## # A tibble: 1 x 3 + ## statistic p.value method + ## + ## 1 0.754 1.45e-65 Shapiro-Wilk normality test -いずれの結果も統計的有意に帰無仮説を棄却するものでした。続いて正規確率グラフを作成します。このグラフでは、度数分布図で得られた度数の累積値より作成される累積度数分布図の縦軸を変形したもので、データが正規分布に従うのであれば直線上に並ぶというものです。 +{:.input_area} +```R +shapiro.test(log(sample(df_lp_kanto$acreage, 5000), base = 10)) %>% tidy() +``` + ## # A tibble: 1 x 3 + ## statistic p.value method + ## + ## 1 0.759 3.81e-65 Shapiro-Wilk normality test +いずれの結果も統計的有意に帰無仮説を棄却するものでした。続いて正規確率グラフを作成します。このグラフでは、度数分布図で得られた度数の累積値より作成される累積度数分布図の縦軸を変形したもので、データが正規分布に従うのであれば直線上に並ぶというものです。 {:.input_area} ```R @@ -116,28 +130,29 @@ ggplot(df_lp_kanto, aes(sample = log(acreage, base = 10))) + stat_qq_line() ``` - ![](../images/qq_plot-1.png) 対数変換により小さな値の集団は正規分布に近づきましたが、途中から大きくずれてしまいました。これはデータの平均と分散の大きさを反映した結果です。 -分散が平均に依存しないように変換をする方法として分散安定変換があります。ポアソン分布に従うデータ $X$ を平方根で変換すると $\tilde{X} = \sqrt{X}$ となり、平均とは無関係に分散がほぼ一定になります。 +分散が平均に依存しないように変換をする方法として分散安定変換があります。ポアソン分布に従うデータ \(X\) を平方根で変換すると +\(\tilde{X} = \sqrt{X}\) となり、平均とは無関係に分散がほぼ一定になります。 -対数変換と平方根変換を一般化したものとしてBox-Cox変換があります。Box-Cox変換はパラメータ $\lambda$ を指定する必要があり、パラメータ $\lambda$ を変更することで、対数変換 ($\lambda$ = 0)、平方根変換 ($\lambda$ = 0.5)、逆数変換 ($\lambda$ = -1.0) に対応します。Box-Cox変換は次の式で定義されます。 +対数変換と平方根変換を一般化したものとしてBox-Cox変換があります。Box-Cox変換はパラメータ \(\lambda\) +を指定する必要があり、パラメータ \(\lambda\) を変更することで、対数変換 +(\(\lambda\) = 0)、平方根変換 (\(\lambda\) = 0.5)、逆数変換 (\(\lambda\) = -1.0) +に対応します。Box-Cox変換は次の式で定義されます。 -$$ +\[ \tilde{x}=\left\{ \begin{array}{ll} \frac{x^\lambda-1}{\lambda, }&\lambda \neq 0\\ \ln(x)&\lambda = 0 \end{array} \right. -$$ +\] 土地の面積にBox-Cox変換を施し、改めて正規性の検定を行いましょう。 - - {:.input_area} ```R rec_lp_acreage <- @@ -150,16 +165,21 @@ df_lp_acreage_boxcox <- juice(all_predictors()) ``` - - + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? {:.input_area} ```R shapiro.test(sample(df_lp_acreage_boxcox$acreage, 5000)) %>% tidy() ``` - - + ## # A tibble: 1 x 3 + ## statistic p.value method + ## + ## 1 0.987 5.37e-21 Shapiro-Wilk normality test {:.input_area} ```R @@ -169,85 +189,57 @@ df_lp_acreage_boxcox %>% stat_qq_line() ``` - 統計的には正規分布ではないと判断されましたが、正規確率グラフを見るとより多くのデータが正規分布に近づいていることがわかります。最後に、土地面積のデータにさまざまな変換を施したヒストグラムを比較のために作っておきましょう。 ![](../images/qq_plot_after_boxcox_transform-1.png) +![](../images/lp_acreage_transform-1.png) +対数変換やBox-Cox変換は、0や負値を扱えないという欠点もあります。そのため、強引に正の値となるような加算の処理をしてから対数変換を行うことがあります。このような処理に対して、足し合わせる数値を下駄に例えて「下駄を履かせる」と表現することがあります。ただしこの「下駄」も負値についても非負値とするために「…を足す」のような処理をすると解釈が難しくなる問題があります。ここでは詳しく解説しませんが、0や負値を含んだ特徴量を扱えるYeo-Johnson変換 +(Yeo-Johnson Power Transformations) が効果的な時があります。 {:.input_area} ```R -p1 <- - ggplot(df_lp_kanto, aes(acreage)) + - geom_histogram(bins = 30) + - ggtitle("raw") + - aes(fill = ds_col(1)) + - scale_fill_identity() - -p2 <- - rec_lp_acreage %>% - step_sqrt(all_predictors()) %>% - prep(training = df_lp_kanto) %>% - juice(all_predictors()) %>% - ggplot(aes(acreage)) + - geom_histogram(bins = 30) + - ggtitle("sqrt") + - aes(fill = ds_col(2)) + - scale_fill_identity() +# 0 や 負値では対数変換が行えない +log(0) +``` -p3 <- - rec_lp_acreage %>% - step_log(all_predictors(), base = 10) %>% - prep(training = df_lp_kanto) %>% - juice(all_predictors()) %>% - ggplot(aes(acreage)) + - geom_histogram(bins = 30) + - ggtitle("log (base10)") + - aes(fill = ds_col(3)) + - scale_fill_identity() + ## [1] -Inf -p4 <- - df_lp_acreage_boxcox %>% - ggplot(aes(acreage)) + - geom_histogram(bins = 30, show.legend = FALSE) + - ggtitle("Box-Cox") + - aes(fill = ds_col(4)) + - scale_fill_identity() - -plot_grid(p1, p2, p3, p4, ncol = 2, labels = "AUTO") +{:.input_area} +```R +log10(0) ``` + ## [1] -Inf -![](../images/lp_acreage_transform-1.png) - -対数変換やBox-Cox変換は、0や負値を扱えないという欠点もあります。そのため、強引に正の値となるような加算の処理をしてから対数変換を行うことがあります。このような処理に対して、足し合わせる数値を下駄に例えて「下駄を履かせる」と表現することがあります。ただしこの「下駄」も負値についても非負値とするために「...を足す」のような処理をすると解釈が難しくなる問題があります。ここでは詳しく解説しませんが、0や負値を含んだ特徴量を扱えるYeo-Johnson変換 (Yeo-Johnson Power Transformations) が効果的な時があります。 +{:.input_area} +```R +log10(-1) +``` + ## Warning: 計算結果が NaN になりました + ## [1] NaN {:.input_area} ```R -# 0 や 負値では対数変換が行えない -log(0) -log10(0) -log10(-1) # 下駄を履かせる処理 log10(0 + 1) ``` + ## [1] 0 ## ロジット変換 数値データの中には、比率や確率のデータも含まれます。この値は0から1の範囲をとりますが、ロジット変換による特徴量エンジニアリングが適用可能です。ロジット変換は次の式で定義されます。 -$$ +\[ logit(\pi) = \log(\frac{\pi}{1-\pi}) -$$ +\] この変換は0から1の値を負と正の無限大の間の値に変換します。つまり変換後の値は正規分布に近づくことが期待されます。またこの値は逆ロジット変換により元の値へ戻せます。 - - {:.input_area} ```R set.seed(123) @@ -263,11 +255,14 @@ df_transformed_examples <- juice() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? ロジット変換を行うと、元のデータとの散布図を取った時に曲線を描くようになります。 - - {:.input_area} ```R df_transformed_examples %>% @@ -275,7 +270,6 @@ df_transformed_examples %>% geom_point() ``` - ![](../images/trans_logit-1.png) ## より豊かな表現 @@ -288,12 +282,10 @@ df_transformed_examples %>% ### 非線形特徴量 -目的変数と説明変数との関係は常に線形であるとは限りません。次の図は、ビールの支出データより作成した、月間のビール支出金額とその月の平均気温のプロットです。気温が高くなると支出金額も増えているという関係は読めますが、単純な線形関係ではないように見えます。全体に細かな凹凸があったり、平均気温8から10$^\circ$Cのあたりで跳ね上がる、25$^\circ$Cを超えると一気に増加するという傾向を掴めていません。 +目的変数と説明変数との関係は常に線形であるとは限りません。次の図は、ビールの支出データより作成した、月間のビール支出金額とその月の平均気温のプロットです。気温が高くなると支出金額も増えているという関係は読めますが、単純な線形関係ではないように見えます。全体に細かな凹凸があったり、平均気温8から10\(^\circ\)Cのあたりで跳ね上がる、25\(^\circ\)Cを超えると一気に増加するという傾向を掴めていません。 このような非線形の関係を捉えるために、統計学の文脈では線形回帰を拡張した非線形回帰の手法が使われますが、これは機械学習の分析でも有効です。ここでは非線形回帰でよく使われる多項式回帰およびスプライン回帰を利用した特徴量の生成方法を学びます。 - - {:.input_area} ```R beer_temperature_plot <- @@ -302,9 +294,6 @@ beer_temperature_plot <- geom_point() ``` - - - {:.input_area} ```R beer_temperature_plot + @@ -313,7 +302,6 @@ beer_temperature_plot + se = FALSE) ``` - ![](../images/linear_temp_and_expense-1.png) @@ -322,15 +310,13 @@ beer_temperature_plot + #### 多項式回帰 -線形回帰モデルでは1次の式を扱ってきました。線形モデルから非線形のモデルに当てはめようとする時、最も簡単なのは1次式を2次以上の式に変えてしまうことです。$y = \beta_{0}+\beta_{1}x$で表せる線形モデルを3次の式にすると以下のように表現できます。 +線形回帰モデルでは1次の式を扱ってきました。線形モデルから非線形のモデルに当てはめようとする時、最も簡単なのは1次式を2次以上の式に変えてしまうことです。\(y = \beta_{0}+\beta_{1}x\)で表せる線形モデルを3次の式にすると以下のように表現できます。 -$$ +\[ f(x) = \sum_{i=1}^{3}\beta_{i}f_{i}(x) = \beta_{1}x + \beta_{2}x^2 + \beta_{3}x^3 -$$ - -多項式特徴量では、多項式回帰により得られる説明変数 $x$ の2乗項 $x^2$ や3乗項 $$x^3$$ を特徴量として加えます。 - +\] +多項式特徴量では、多項式回帰により得られる説明変数 \(x\) の2乗項 \(x^2\) や3乗項 \[x^3\] を特徴量として加えます。 {:.input_area} ```R @@ -341,11 +327,29 @@ df_beer %>% juice() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 48 x 3 + ## expense temperature_average_poly_1 temperature_average_poly_2 + ## + ## 1 19.8 -0.204 0.173 + ## 2 20.7 -0.206 0.180 + ## 3 22.3 -0.117 -0.0656 + ## 4 24.8 -0.0359 -0.170 + ## 5 30.5 0.0918 -0.104 + ## 6 32.9 0.111 -0.0692 + ## 7 43.3 0.191 0.141 + ## 8 41.8 0.200 0.174 + ## 9 28.1 0.121 -0.0494 + ## 10 24.8 0.0396 -0.165 + ## # … with 38 more rows 実際に1次の線形モデルに多項式回帰を適用するとスムーズな適合が観察できます。先の気温とビールの支出金額の関係をいくつかの次数(2次、3次、極端な例として10次)で当てはめた多項式回帰の結果を比較しましょう。 - - {:.input_area} ```R p1 <- @@ -380,21 +384,19 @@ p3 <- beer_temperature_plot + plot_grid(p1, p2, p3, ncol = 3) ``` - ![](../images/polynomial_temp_and_expense-1.png) 次数を増やすことで曲線の滑らかさは増加します。しかしこのデータをモデルに当てはめると過学習を引き起こしやすくなることが指摘されています。加えて、高次データにおいてはデータが少ない領域や境界周辺で極端な挙動を振る舞いを示す傾向にあります。サポートベクターマシンではカーネル関数を用いることで多項式特徴量のような複雑なデータに対応可能なモデルです。 #### スプライン回帰 -多項式の次数を増やすことで、望ましくない波状を生み出してしまいます。この問題に対して、スプライン回帰はノット (knots) と呼ばれる一連の予測変数の固定点領域を滑らかに補間する方法を提供します。 +多項式の次数を増やすことで、望ましくない波状を生み出してしまいます。この問題に対して、スプライン回帰はノット (knots) +と呼ばれる一連の予測変数の固定点領域を滑らかに補間する方法を提供します。 ノットは均等にデータが含まれる領域を分割するように働きます。例えば3ノットのスプラインは33.3%、66.7%の分位点を配置します。この分位点は多項式回帰の結び目として利用されます。そのためスプライン回帰ではパラメータとして多項式の次数とノットの位置を指定する必要があります。 スプライン回帰の注意として、係数の解釈はできない点があります。また最適なノット数を選択するのに、モデルのデータが1次であれば視覚化により確かめられます。あるいは交差検証、一般化交差検証を使用することが可能です。 - - {:.input_area} ```R knots <- @@ -410,20 +412,8 @@ beer_temperature_plot + color = ds_col(5)) ``` - ![](../images/spline_temp_and_expense-1.png) - - -{:.input_area} -```R -# splines::bs(df_beer$temperature_average, knots = knots) %>% -# tidy() -``` - - - - {:.input_area} ```R df_beer %>% @@ -433,79 +423,70 @@ df_beer %>% juice() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 48 x 3 + ## expense temperature_average_ns_1 temperature_average_ns_2 + ## + ## 1 19.8 0.0699 -0.0470 + ## 2 20.7 0.0636 -0.0428 + ## 3 22.3 0.337 -0.210 + ## 4 24.8 0.517 -0.255 + ## 5 30.5 0.557 0.0469 + ## 6 32.9 0.538 0.131 + ## 7 43.3 0.422 0.537 + ## 8 41.8 0.405 0.590 + ## 9 28.1 0.527 0.176 + ## 10 24.8 0.579 -0.136 + ## # … with 38 more rows ## ビン詰め (ビニング binning) ビン詰めあるいは離散化は、量的変数を1組あるいはそれ以上のカテゴリに変換する作業です。ここで新たに「ビン」という言葉が出てきました。ビンはカテゴリの値が入る容器だと考えてください。ヒストグラムで使われる「ビン」と同じものです。数値データを扱う際、単一の変数の四分位数を求めることがありますが、それと同じような作業です。四分位数ではデータを4つに区切ります。 - - -{:.input_area} -```R -set.seed(123) -df <- data.frame(y = rnorm(100)) - -df_quantile <- - quantile(df$y, probs = c(0.25, 0.50, 0.75)) %>% - as.data.frame() %>% - as_tibble() %>% - purrr::set_names("y") %>% - mutate(label = c("Bottom 25%", "Median", "Top 75%")) - -df <- - df %>% - mutate(grp = case_when( - y < df_quantile$y[1] ~ 1, - y < df_quantile$y[2] ~ 2, - y < df_quantile$y[3] ~ 3) %>% - tidyr::replace_na(4) %>% - as.character()) - -df %>% - ggplot(aes(x = "", y = y)) + - geom_violin() + - geom_jitter(aes(color = grp), show.legend = FALSE) + - geom_text(data = df_quantile, - aes(x = 1.4, - y = y, - label = label), - nudge_x = 0.1) + - scale_color_ds() -``` - - ![](../images/plot_quantile-1.png) すべてのデータが離散化に適しているのではありませんが、離散化が効果を発揮する場面として次の状況が考えられます。 -- 結果の解釈が簡単になる -- スケールによる影響を緩和する -- データの変動を抑える + - 結果の解釈が簡単になる + - スケールによる影響を緩和する + - データの変動を抑える ビン詰めにはいろいろな方法があります。固定幅や分位数を用いるもの、教師なしの方法などです。 -地価公示データには、最寄駅からの距離が `distance_from_station` として与えられています。これはいくつかのカテゴリに分けて考えると良いかもしれません。 +地価公示データには、最寄駅からの距離が `distance_from_station` +として与えられています。これはいくつかのカテゴリに分けて考えると良いかもしれません。 ### 固定幅による離散化 -- 各階級の範囲をあらかじめ決めておく -- 階級区分に規則性がある場合とない場合 - - 年齢... 10歳区切り。階級幅は一定で規則性がある - - 年齢... ライフスタイルによる区切り。規則性がない -- 10の累乗 (0~9, 10~99, 100~9999) 指数関数的 + - 各階級の範囲をあらかじめ決めておく + - 階級区分に規則性がある場合とない場合 + - 年齢… 10歳区切り。階級幅は一定で規則性がある + - 年齢… ライフスタイルによる区切り。規則性がない + - 10の累乗 (0~9, 10~99, 100~9999) 指数関数的 最寄駅からの距離をいくつかのカテゴリに分けてみましょう。ここでは距離に応じて以下のカテゴリを設定します。 -- 近距離... 800m以内 -- 中距離... 800m~2km -- 遠距離... 2km以上 - + - 近距離… 800m以内 + - 中距離… 800m~2km + - 遠距離… 2km以上 + {:.input_area} ```R df_lp_kanto$distance_from_station %>% summary() +``` + + ## Min. 1st Qu. Median Mean 3rd Qu. Max. + ## 0 500 1000 1566 1800 24000 +{:.input_area} +```R df_lp_kanto_dist_c <- df_lp_kanto %>% arrange(distance_from_station) %>% @@ -515,7 +496,10 @@ df_lp_kanto_dist_c <- distance_from_station > 2000 ~ "遠距離" ) %>% forcats::fct_inorder()) +``` +{:.input_area} +```R df_lp_kanto_dist_c %>% group_by(distance_cat) %>% summarise(posted_land_price = mean(posted_land_price)) %>% @@ -523,12 +507,13 @@ df_lp_kanto_dist_c %>% geom_bar(stat = "identity") ``` +![](../images/lp_categorize_distance-1.png) ### 分位数による離散化 -- 均等に分割 - + - 均等に分割 + {:.input_area} ```R @@ -542,8 +527,9 @@ binned <- table(predict(binned, df_lp_kanto$distance_from_station)) ``` - - + ## + ## distance_bins1 distance_bins2 distance_bins3 distance_bins4 distance_bins5 + ## 1710 1684 1714 1673 1695 {:.input_area} ```R @@ -553,12 +539,35 @@ df_lp_kanto %>% options = list(cuts = 3)) %>% prep() %>% juice() - -df_lp_kanto %>% - ggplot(aes(distance_from_station, posted_land_price)) + - geom_point() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 8,476 x 1 + ## distance_from_station + ## + ## 1 bin3 + ## 2 bin3 + ## 3 bin3 + ## 4 bin3 + ## 5 bin1 + ## 6 bin3 + ## 7 bin2 + ## 8 bin2 + ## 9 bin1 + ## 10 bin1 + ## # … with 8,466 more rows + +{:.input_area} +```R +# df_lp_kanto %>% +# ggplot(aes(distance_from_station, posted_land_price)) + +# geom_point() +``` @@ -576,8 +585,6 @@ df_lp_kanto %>% ビールの売り上げについて気温と湿度の交互作用を考えてみましょう。これは気温がビールの売り上げに及ぼす影響を湿度が調整すると考えるものです。 - - {:.input_area} ```R df_lp_kanto %>% @@ -589,8 +596,7 @@ df_lp_kanto %>% geom_point() ``` - - +![](../images/lp_interactions-1.png) {:.input_area} ```R @@ -601,13 +607,30 @@ df_beer2018q2 %>% juice() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 92 x 4 + ## temperature_avera… humidity_average_… expense temperature_average_x_hum… + ## + ## 1 28.1 75 58.0 2108. + ## 2 28.8 70 42.8 2016 + ## 3 28.4 69 23.9 1960. + ## 4 27.6 75 28.6 2070 + ## 5 26.7 79 35.4 2109. + ## 6 21.7 99 29.1 2148. + ## 7 25 86 53.9 2150 + ## 8 27.4 79 61.6 2165. + ## 9 27.3 86 38.4 2348. + ## 10 28.5 78 33.1 2223 + ## # … with 82 more rows 交互作用特徴量は複数の特徴量の積で計算されます。例えば数値変数どうしであれば単純な掛け算です。二値特徴量間では論理積となります。 ## 次元集約によると特徴量の作成 -- PCA, ICA -- L2ノルム正則化... ベクトル空間に対して「距離」を与えるための数学の道具 + - PCA, ICA + - L2ノルム正則化… ベクトル空間に対して「距離」を与えるための数学の道具 過学習を防ぐためでもある @@ -620,11 +643,18 @@ df_beer2018q2 %>% ## 関連項目 -- [特徴量選択](feature-selection) + - [特徴量選択](feature-selection) ## 参考文献 -- Max Kuhn and Kjell Johnson (2013). Applied Predictive Modeling. (Springer) -- Sarah Guido and Andreas Müller (2016). Introduction to Machine Learning with Python A Guide for Data Scientists (O'Reilly) (**翻訳** 中田秀基訳 (2017). Pythonではじめる機械学習 scikit-learnで学ぶ特徴量エンジニアリングと機械学習の基礎 (オライリー)) -- Peter Bruce and Andrew Bruce (2017). Practical Statistics for Data Scientist - 50 Essential Concepts (O'Reilly) (**翻訳** 黒川利明訳(2018). データサイエンスのための統計学入門 - 予測、分類、統計モデリング、統計的機械学習とRプログラミング (オライリー)) -- Max Kuhn and Kjell Johnson (2019). Feature Engineering and Selection: A Practical Approach for Predictive Models (CRC Press) + - Max Kuhn and Kjell Johnson (2013). Applied Predictive Modeling. + (Springer) + - Sarah Guido and Andreas Müller (2016). Introduction to Machine + Learning with Python A Guide for Data Scientists (O’Reilly) (**翻訳** + 中田秀基訳 (2017). Pythonではじめる機械学習 scikit-learnで学ぶ特徴量エンジニアリングと機械学習の基礎 + (オライリー)) + - Peter Bruce and Andrew Bruce (2017). Practical Statistics for Data + Scientist - 50 Essential Concepts (O’Reilly) (**翻訳** 黒川利明訳(2018). + データサイエンスのための統計学入門 - 予測、分類、統計モデリング、統計的機械学習とRプログラミング (オライリー)) + - Max Kuhn and Kjell Johnson (2019). Feature Engineering and + Selection: A Practical Approach for Predictive Models (CRC Press) diff --git a/_build/02/spatial-data.md b/_build/02/spatial-data.md index d788282..a9dcfe7 100644 --- a/_build/02/spatial-data.md +++ b/_build/02/spatial-data.md @@ -9,57 +9,11 @@ prev_page: next_page: url: /03/readme title: 'より良いモデルを目指して' +output: github_document + comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" --- - - -{:.input_area} -```R -source(here::here("R/setup.R")) -source(here::here("R/prepared_landprice.R")) -``` - - -{:.output .output_traceback_line} -``` - - Error: Target `df_lp_kanto_sp_baked` failed. Call `diagnose(df_lp_kanto_sp_baked)` for details. Error message: - オブジェクト 'station' がありません - Traceback: - - - 1. source(here::here("R/prepared_landprice.R")) - - 2. withVisible(eval(ei, envir)) - - 3. eval(ei, envir) - - 4. eval(ei, envir) - - 5. drake::make(plan_land_price_spatial_fe) - - 6. process_targets(config) - - 7. run_native_backend(config) - - 8. get(paste0("backend_", parallelism), envir = getNamespace("drake"))(config) - - 9. loop_build(target = targets[i], config = config, downstream = targets[-seq_len(i)]) - - 10. conclude_build(build = build, config = config) - - 11. handle_build_exceptions(target = target, meta = meta, config = config) - - 12. drake_error("Target `", target, "` failed. Call `diagnose(", - . target, ")` for details. Error message:\n ", meta$error$message, - . config = config) - - 13. stop(..., call. = FALSE) - - -``` - # 地理空間データの取り扱い 地理空間データは、スマートフォンやSNSの発達、各種センサーの普及に伴い利用機会が増えています。従来の統計調査や自然環境調査だけでなく、オープンデータやログデータなど、扱う媒介も増えてきており多様な分野・領域で扱われるデータ形式の1つです。なおここでいう地理空間データとは、なんらかのデータに対して地球上の空間的な位置を記録した位置情報を指します。 @@ -78,8 +32,8 @@ source(here::here("R/prepared_landprice.R")) ベクタデータは、現実世界に存在する地物(行政区域など実際には存在しないもの)をデータ上に示したものを指します。ベクタの週類は複数あり、特に点、線、面を扱う場面が多いです。地物は座標と属性情報をもち、地図上で表現可能です。この属性情報は、一般的にテーブル形式で格納され、図形情報と関連づけられています。 -- 座標と属性情報をもち、地図上で表現できる -- ポリゴンは、(x,y)の座標値を結んだ閉じた線分として格納される + - 座標と属性情報をもち、地図上で表現できる + - ポリゴンは、(x,y)の座標値を結んだ閉じた線分として格納される ラスタデータは主題データ、連続データとも呼ばれ、一定領域を格子状のグリッドで囲んだデータになります。ラスタを構成する各グリッドには値が記録されていて、ピクセルに値を記録する画像データのような情報のまとまりとなっています。ラスタは時系列で重ねることができ、時系列の解析にも利用されます。 @@ -87,28 +41,34 @@ source(here::here("R/prepared_landprice.R")) ### 座標参照系 -地球上の地物の位置を示すために、座標に加えて座標参照系 (Coordinate Reference System: CRS) の定義が必要になります。これは二次元の座標を地球上に投影するため、位置関係と紐づけて使われます。言い換えると、座標参照系が判明している時にのみ座標を地球の表面に置くことが可能です。 +地球上の地物の位置を示すために、座標に加えて座標参照系 (Coordinate Reference System: CRS) +の定義が必要になります。これは二次元の座標を地球上に投影するため、位置関係と紐づけて使われます。言い換えると、座標参照系が判明している時にのみ座標を地球の表面に置くことが可能です。 -「座標」にはXY座標を使ったものに加えて、緯度経度で表現されるものもあります (地理座標系)。また地図投影法により定義された値 (投影座標系)を座標として使うこともあります。 +「座標」にはXY座標を使ったものに加えて、緯度経度で表現されるものもあります (地理座標系)。また地図投影法により定義された値 +(投影座標系)を座標として使うこともあります。 地図上に地物を重ねる際、意図しない箇所に地物が表示されることがあります。これは地図データの座標系が異なることが原因で発生する問題の一つです。加えて座標系が異なるデータは普通距離が計算できません。この問題については後述します。 ふだん扱う機会の多い、主要な座標参照系として次のものがあります。 -- 地理座標系 - - SRID: 4326 (WGS84) 世界測地系、GPSなど - - SRID: 6668 (JDG2011) 日本測地系2011。東北地方太平洋沖地震による東日本での地殻変動の影響を反映 - - SRID: 4612 (JDG2000) 日本測地系2000。 -- 投影座標系... 平面直角座標系 - - SRID: 6669-6687 (JDG2011) 日本の公共測量で採用されている座標系。日本全体を19の地域に分割し、平面投影上の歪みを軽減している + - 地理座標系 + - SRID: 4326 (WGS84) 世界測地系、GPSなど + - SRID: 6668 (JDG2011) 日本測地系2011。東北地方太平洋沖地震による東日本での地殻変動の影響を反映 + - SRID: 4612 (JDG2000) 日本測地系2000。 + - 投影座標系… 平面直角座標系 + - SRID: 6669-6687 (JDG2011) + 日本の公共測量で採用されている座標系。日本全体を19の地域に分割し、平面投影上の歪みを軽減している ### 空間的影響 -地域という空間的な広がりやスケールを持つ対象を扱う地理空間データには、他のデータにはない独自の効果 (空間的影響)が存在します。空間的自己相関 (spatial autocorrelation) と空間的異質性 (spatial heterogeneity) です。 +地域という空間的な広がりやスケールを持つ対象を扱う地理空間データには、他のデータにはない独自の効果 (空間的影響)が存在します。空間的自己相関 +(spatial autocorrelation) と空間的異質性 (spatial heterogeneity) +です。 空間的自己相関は時系列データの自己相関のように、互いに近い距離にあるものはより強く影響し合うという考え方です。実際に、人口が集中する地域は楕円状に人口が拡散し、円の外側ほど人口が少ないことがしばしば観察されます。そしてもう一つ、空間的異質性は、空間データの空間的な分散を仮定したものです。つまり、地域が異なれば傾向も異なるという特徴になります。 -一般に、回帰モデルでは誤差項 $\epsilon$ が独立で正規分布に従うと仮定されます。ここでの分散均一は、地理空間データでは成立しないことがあります。ここで分散均一としてしまうと、地域性を考慮しないことになってしまいます。そこで地理空間データの回帰モデルでは分散不均一を前提としたモデルが組まれます。 +一般に、回帰モデルでは誤差項 \(\epsilon\) +が独立で正規分布に従うと仮定されます。ここでの分散均一は、地理空間データでは成立しないことがあります。ここで分散均一としてしまうと、地域性を考慮しないことになってしまいます。そこで地理空間データの回帰モデルでは分散不均一を前提としたモデルが組まれます。 加えて、これらの問題は地理空間データに対して[交差検証](../03/data-splitting)を行う際にも発生します。これについては交差検証の章で議論しますが、地理的に偏りのあるデータをランダムに分割してしまうことで過学習やデータ漏洩に繋がる不安があります。整理すると、空間データの分析には時系列データのように特殊性があり、特に地域性や近隣を考慮した分析の設計が必要になります。 @@ -122,9 +82,8 @@ source(here::here("R/prepared_landprice.R")) 最後に数値がありますが、これが住所であることは多くの日本人が理解できると思います。一方機械では、[日付・時間データの取り扱い](../02/date-and-time)でみたように基本的にはただの文字列として扱い、構成要素を指定しない限りは住所を分解してくれません。 -位置情報として住所が利用可能な時、パターンマッチを使った分割で住所の要素を分解可能です。これは[カテゴリデータ](../02/categorical)の特徴量エンジニアリングで触れた、Expansion encodingになります。例を見てみましょう。 - - +位置情報として住所が利用可能な時、パターンマッチを使った分割で住所の要素を分解可能です。これは[カテゴリデータ](../02/categorical)の特徴量エンジニアリングで触れた、Expansion +encodingになります。例を見てみましょう。 {:.input_area} ```R @@ -132,6 +91,8 @@ source(here::here("R/prepared_landprice.R")) stringr::str_split("(?<=東京都|道|府|県)", n = 2) ``` + ## [[1]] + ## [1] "東京都" "千代田区大手町1-1" これは都道府県名と以下の住所を分割する処理です。必要とする情報はどのレベルまででしょうか。もし市区町村の情報が必要なのであれば、さらなる分割を試みることになります。しかし、都道府県を分離するようなパターンマッチは実装が容易ですが、市区町村、さらに以下の地名を分割していくことは次第に困難になります。対応すべきパターンと例外が出てくるためです。このような場合、あらかじめ住所文字列が分割された辞書を利用することが、近道となることがあります。 @@ -141,24 +102,24 @@ source(here::here("R/prepared_landprice.R")) 次は、地理空間データをより地理空間データらしく扱うためのコーディングです。住所ではあくまでも文字列でしたが、位置情報に対して地理座標を付加することで空間的な関係が議論できるようになります。こうした各種の情報から座標を付与する、特定する作業をジオコーディングと呼びます。都道府県の名前は一定の面積をもつポリゴンの座標に変換可能なので、ジオコーディングを行うことができます。対して、特定の座標から住所や地名、メッシュコードを判別させる作業を逆ジオコーディングと呼びます。ジオコーディングと逆ジオコーディングは一般に相互変換が可能な処理です。 -ここでは緯度経度をメッシュコードに, メッシュコードから緯度経度にそれぞれ変換する処理を示します。メッシュコード (標準地域コード、標準地域メッシュ)は国勢調査などの、国内全域を調査するために用いられるポリゴンのことで、規模により名称が異なります。一片の長さがおよそ80kmの第1次地域区画がもっとも大きく、10km、1kmと小さくなっていきます。 +ここでは緯度経度をメッシュコードに, メッシュコードから緯度経度にそれぞれ変換する処理を示します。メッシュコード +(標準地域コード、標準地域メッシュ)は国勢調査などの、国内全域を調査するために用いられるポリゴンのことで、規模により名称が異なります。一片の長さがおよそ80kmの第1次地域区画がもっとも大きく、10km、1kmと小さくなっていきます。 メッシュコードには位置を特定するための番号が割り振られています。メッシュの大きさによりメッシュコードの長さが異なります。これを使うことで地理空間データとして活用できるようになります。次の表は、標準地域メッシュの体系とメッシュコードの関係を表したものです。 -| 地域区画 | 標準地域メッシュ| 規模 | 地域メッシュコードの例 | -|----------|------------------|-----|:-----------------------| -| 第1次地域区画 | | 80km | 5438 | -| 第2次地域区画 | (10倍地域メッシュ) | 10km | 543823 | -| | 5倍地域メッシュ | 5km | 5438234 | -| | 2倍地域メッシュ | 2km | 543823645 | -| 第3次地域区画 | 基準地域メッシュ | 1km | 54382343 | -| | 1/2地域メッシュ | 500m | 543823431 | -| | 1/4地域メッシュ | 250m | 5438234312 | -| | 1/8地域メッシュ | 125m | 54382343123 | - -土砂災害・雪崩メッシュデータに含まれる メッシュコード (`meshCode`) は桁数が7です。つまり5kmの範囲をカバーするメッシュコードだということがわかります。 - +| 地域区画 | 標準地域メッシュ | 規模 | 地域メッシュコードの例 | +| ------- | ----------- | ---- | :---------- | +| 第1次地域区画 | | 80km | 5438 | +| 第2次地域区画 | (10倍地域メッシュ) | 10km | 543823 | +| | 5倍地域メッシュ | 5km | 5438234 | +| | 2倍地域メッシュ | 2km | 543823645 | +| 第3次地域区画 | 基準地域メッシュ | 1km | 54382343 | +| | 1/2地域メッシュ | 500m | 543823431 | +| | 1/4地域メッシュ | 250m | 5438234312 | +| | 1/8地域メッシュ | 125m | 54382343123 | +土砂災害・雪崩メッシュデータに含まれる メッシュコード (`meshCode`) +は桁数が7です。つまり5kmの範囲をカバーするメッシュコードだということがわかります。 {:.input_area} ```R @@ -166,11 +127,23 @@ df_hazard %>% select(meshCode) ``` + ## # A tibble: 4,315 x 1 + ## meshCode + ## + ## 1 3927771 + ## 2 3927252 + ## 3 3927251 + ## 4 3927252 + ## 5 3927263 + ## 6 3927254 + ## 7 3927774 + ## 8 3927154 + ## 9 3927251 + ## 10 3927251 + ## # … with 4,305 more rows ではメッシュコードからメッシュコードの座標を取得してみましょう。 - - {:.input_area} ```R library(jpmesh) @@ -182,24 +155,23 @@ sf_hazard <- sf_hazard$geometry[[1]] ``` + ## POLYGON ((127.875 26.58333, 127.9375 26.58333, 127.9375 26.625, 127.875 26.625, 127.875 26.58333)) -RのsfパッケージはRでのベクタ形式の地理空間オブジェクトを操作するのに優れています。メッシュコードの座標はポリゴンとして `geometry` に格納されています。 +RのsfパッケージはRでのベクタ形式の地理空間オブジェクトを操作するのに優れています。メッシュコードの座標はポリゴンとして `geometry` +に格納されています。 では今度は緯度経度からメッシュコードを判別させる逆ジオコーディングを行います。 - - {:.input_area} ```R mesh <- coords_to_mesh(139.7671, 35.6812) mesh ``` + ## [1] "53394611" メッシュコードが得られましたが、これは地図上に表示してみないことにはどこだかわかりにくいです。確認のために地図上にマッピングしてみましょう。 - - {:.input_area} ```R library(mapview) @@ -207,13 +179,10 @@ mapview(export_mesh(mesh)) + mapview(st_sfc(st_point(c(139.7671, 35.6812)), crs = 4326)) ``` - ![](../images/meshcode_mapping.png) 座標からの逆ジオコーディングは、データの空間的な配置を特徴量として活用するために有効です。メッシュコードのような変数をカウントし、期間中に災害発生が起こりやすい場所を集計し、モデルに組み込めるかもしれません。これを確かめるために土砂災害・雪崩メッシュデータから、災害種別でのメッシュ件数をカウントした値を表示してみます。 - - {:.input_area} ```R df_hazard_type <- @@ -231,7 +200,6 @@ ggplot() + facet_wrap(~ hazardType, ncol = 2) ``` - ![](../images/hazard_type_mesh5km-1.png) やはり災害発生は種類により地域間での差がありそうです。一方で回数には大きな差がないかもしれません(ただしグラフからは読み取れなくなっていますが、最大で60件以上発生しているメッシュがあることも注意が必要です)。 @@ -253,24 +221,16 @@ ggplot() + 乗車人数を加えてみることにします。 - - -{:.input_area} -```R -df_lp_kanto -``` - - ### 参照座標系が異なるために生じる問題 - ## まとめ ## 関連項目 ## 参考文献 -- 谷村晋 (2010). 地理空間データ分析 (共立出版) -- 古谷知之 (2011). Rによる空間データの統計分析 (朝倉書店) -- 瀬谷創、堤盛人 (2014). 空間統計学: 自然科学から人文・社会科学まで (朝倉書店) -- Robin Lovelace, Jakub Nowosad, Jannes Muenchow (2019). Geocomputation with R (CRC Press) + - 谷村晋 (2010). 地理空間データ分析 (共立出版) + - 古谷知之 (2011). Rによる空間データの統計分析 (朝倉書店) + - 瀬谷創、堤盛人 (2014). 空間統計学: 自然科学から人文・社会科学まで (朝倉書店) + - Robin Lovelace, Jakub Nowosad, Jannes Muenchow (2019). + Geocomputation with R (CRC Press) diff --git a/_build/02/text.md b/_build/02/text.md index ef88c7f..28404cf 100644 --- a/_build/02/text.md +++ b/_build/02/text.md @@ -9,24 +9,17 @@ prev_page: next_page: url: /02/date-and-time title: '日付・時間データの取り扱い' +output: github_document + comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" --- - - -{:.input_area} -```R -source(here::here("R/setup.R")) -``` - - - - {:.input_area} ```R library(textrecipes) ``` + # テキストデータの取り扱い @@ -34,21 +27,36 @@ library(textrecipes) テキストデータを扱ううえで大事になってくるのは、文字の長さや区切り位置の有無、そして言語です。テキストデータ、つまり文書の処理は自然言語処理の領域に入ります。しかしいくつかの基本的な作業はデータ分析にも応用可能であり、カテゴリと同じく特徴量エンジニアリングにより数値化が可能です。 -テキストデータの例として、地価公示データに記録されている土地利用の状況 (`surrounding_present_usage`) を例にします。このデータは比較的短文で、重複のあるデータで、本来はカテゴリとみなせるものです。しかし説明を簡単にするため、そしてこれまでのデータセットを利用し続けるために用います。また本来テキストデータとして扱う文字数はこれよりも多いはずですが、基本的な処理は共通です。 +テキストデータの例として、地価公示データに記録されている土地利用の状況 (`surrounding_present_usage`) +を例にします。このデータは比較的短文で、重複のあるデータで、本来はカテゴリとみなせるものです。しかし説明を簡単にするため、そしてこれまでのデータセットを利用し続けるために用います。また本来テキストデータとして扱う文字数はこれよりも多いはずですが、基本的な処理は共通です。 またここでは我々が普段扱う日本語のテキストデータに対象を制限しますが、時には英語を扱うこともあります。自然言語処理の分野では言語により、扱い方が異なります。特に日本語や中国語など、単語境界をもたない言語に対しては形態素解析などの前処理が必要になります。これらについてはここでの説明範囲を超えるので参考資料をみてください。 ## 周辺の土地利用の状況 -周辺の土地利用の状況 (`surrounding_present_usage`) は地価公示データのすべてのデータに対して与えられています。ユニークな件数を数えるとおよそデータの半分の数になります。 +周辺の土地利用の状況 (`surrounding_present_usage`) +は地価公示データのすべてのデータに対して与えられています。ユニークな件数を数えるとおよそデータの半分の数になります。 地価公示データの中から、周辺の土地利用の状況が記載された`surrounding_present_usage`を例に解説していきます。まずはこの変数にどのような文字情報が含まれているか確認しておきましょう。 -```{r, eval=TRUE} +{:.input_area} +```R na.omit(df_lp_kanto$surrounding_present_usage)[seq_len(10)] ``` -```{r, eval=TRUE} + ## [1] "低層別荘が建ち並ぶ環境の良い別荘地域" + ## [2] "一般住宅の中にアパート等が混在する住宅地域" + ## [3] "土産店、小売店舗等が多い温泉地の商業地域" + ## [4] "小規模別荘が多く見られる熟成度の劣る別荘地域" + ## [5] "中規模一般住宅が多い国道背後の住宅地域" + ## [6] "一般住宅、アパート等が混在する既成住宅地域" + ## [7] "飲食店や住宅等が混在する路線商業地域" + ## [8] "一般住宅、工場等が混在する傾斜地の住宅地域" + ## [9] "小売店舗や金融機関が混在する既存の商業地域" + ## [10] "一般住宅、店舗等が混在する既成住宅地域" + +{:.input_area} +```R df_lp_surrounding_present_usage_count <- df_lp_kanto %>% count(surrounding_present_usage, sort = TRUE) @@ -56,7 +64,10 @@ df_lp_surrounding_present_usage_count <- nrow(df_lp_surrounding_present_usage_count) ``` -```{r lp_surrounding_present_usage_nchar, eval=FALSE, echo=TRUE} + ## [1] 4397 + +{:.input_area} +```R df_lp_kanto %>% mutate(nchar = as.character(stringr::str_length(surrounding_present_usage))) %>% count(nchar) %>% @@ -77,8 +88,8 @@ df_lp_kanto %>% ### 不要な文字列の削除 -- ハッシュタグ -- URL + - ハッシュタグ + - URL 特定の文字列に変換するという手法も用いられます。対象の文字列を別のものに変える、すなわち置換の処理は正規表現によるパターンマッチを通して行われるのが一般的です。 @@ -94,23 +105,44 @@ df_lp_kanto %>% 「表記揺れ」と呼びます。ユニコード正規化はこうした表記の揺れを修正するために使われる方法です。 -```{r, eval=TRUE, echo=TRUE} +{:.input_area} +```R df_lp_kanto %>% filter(str_detect(surrounding_present_usage, "IC")) %>% pull(surrounding_present_usage) +``` + + ## [1] "中規模の工場と倉庫が多く、ICに近い工業地域" + ## [2] "低層の店舗等が集まるIC近くの路線商業地域" + ## [3] "大中規模倉庫等が建ち並ぶICに近い工業地域" +{:.input_area} +```R df_lp_prep <- df_lp_kanto %>% recipe(~ .) %>% step_stri_trans(surrounding_present_usage, trans_id = "nfkc") %>% prep(strings_as_factors = FALSE) %>% juice() +``` + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? +{:.input_area} +```R df_lp_prep %>% filter(str_detect(surrounding_present_usage, "IC")) %>% pull(surrounding_present_usage) ``` + ## [1] "中規模の工場と倉庫が多く、ICに近い工業地域" + ## [2] "低層の店舗等が集まるIC近くの路線商業地域" + ## [3] "大中規模倉庫等が建ち並ぶICに近い工業地域" + ユニコード正規化は完全ではありません。また、どのような文字を「揺れ」とみなすかは分析者によって異なる可能性もあります。完全な表記揺れに対応するには、データに応じて辞書が必要になるでしょう。 ### 文の分割 (トークン化) @@ -121,24 +153,32 @@ df_lp_prep %>% 分かち書き -```{r, eval= TRUE} +{:.input_area} +```R x <- na.omit(df_lp_kanto$surrounding_present_usage)[1] -tokenizer +tokenizers::tokenize_words(x) ``` + ## [[1]] + ## [1] "低層" "別荘" "が" "建ち並ぶ" "環境" "の" + ## [7] "良い" "別荘" "地域" + ## Bag-of-Words -* 文書を単語の出現回数のベクトルで表現。 - * 単語が文書中に現れない場合、対応する要素の値は0になる - * 単語の並び、階層の概念を表現しない。Bag-of-wordsでこれらの意味はない - * →テキストの意味を正しく理解したい場合にはあまり役立たない - * Bag-of-n-Grams -* 全ての単語を同じようにしてカウントすると、必要以上に強調される単語が出る - * 単純な出現頻度だけでは文書の特徴を表現できない - * 「意味のある」単語が強調されるような特徴を表現する方法を用いるべき + - 文書を単語の出現回数のベクトルで表現。 + - 単語が文書中に現れない場合、対応する要素の値は0になる + - 単語の並び、階層の概念を表現しない。Bag-of-wordsでこれらの意味はない + - →テキストの意味を正しく理解したい場合にはあまり役立たない + - Bag-of-n-Grams + - 全ての単語を同じようにしてカウントすると、必要以上に強調される単語が出る + - 単純な出現頻度だけでは文書の特徴を表現できない + - 「意味のある」単語が強調されるような特徴を表現する方法を用いるべき -```{r, eval = TRUE} + + +{:.input_area} +```R x <- na.omit(df_lp_kanto$surrounding_present_usage)[1] tibble::tibble(x) %>% @@ -147,8 +187,20 @@ tibble::tibble(x) %>% dplyr::count(word, sort = FALSE) ``` + ## # A tibble: 8 x 2 + ## word n + ## + ## 1 低層 1 + ## 2 別荘 2 + ## 3 が 1 + ## 4 建ち並ぶ 1 + ## 5 環境 1 + ## 6 の 1 + ## 7 良い 1 + ## 8 地域 1 -```{r, eval=TRUE} +{:.input_area} +```R text_rec <- df_lp_kanto %>% recipe(~ surrounding_present_usage + .row_id) %>% @@ -159,23 +211,45 @@ df_lp_token <- prep() %>% juice() %>% tidyr::unnest(cols = c(surrounding_present_usage)) +``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + +{:.input_area} +```R df_lp_token %>% filter(.row_id == "13534") ``` + ## # A tibble: 9 x 2 + ## surrounding_present_usage .row_id + ## + ## 1 低層 13534 + ## 2 別荘 13534 + ## 3 が 13534 + ## 4 建ち並ぶ 13534 + ## 5 環境 13534 + ## 6 の 13534 + ## 7 良い 13534 + ## 8 別荘 13534 + ## 9 地域 13534 + ## 単語の除去 Bag-of-Wordsでは、対象の変数に含まれる単語を元に特徴量が生成されますが、中には価値のない単語も含まれます。こうした単語をあらかじめ取り除いておくことは、特徴選択におけるフィルタ法の作業に相当します。 文章の特徴を反映しないような前置詞や冠詞などはその代表です。データ全体で出現頻度の少ない単語も役立つ可能性は低いです。こうした不要な単語が特徴量に含まれないよう、あらかじめ有用でない単語を除去するという方法が取られます。 - ### ストップワードによる単語除去 文書の内容に重要でないと考えられる単語をまとめて対象から除外する処理にストップワードが用いられます。ここでの重要でない、は文書中に頻繁に出現する句読点や「です」「ます」などの単語などです。ストップワードの選別には、言語に固有のリストを使うか、データセットに応じた頻度の高い単語のリストを作成するか、という選択肢があります。 -```{r, eval = TRUE} +{:.input_area} +```R df_lp_kanto %>% recipe(~ .) %>% step_rm(-acreage, -surrounding_present_usage) %>% @@ -188,11 +262,39 @@ df_lp_kanto %>% count(surrounding_present_usage, sort = TRUE) ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 504 x 2 + ## surrounding_present_usage n + ## + ## 1 地域 8430 + ## 2 一般 5215 + ## 3 建ち並ぶ 2890 + ## 4 店舗 2003 + ## 5 商業 1962 + ## 6 中規模 1861 + ## 7 多い 1785 + ## 8 する 1773 + ## 9 られる 1755 + ## 10 見 1736 + ## # … with 494 more rows + ### 出現頻度による単語のフィルタ 出現頻度の少ない単語も解析の際に障害となる可能性があります。これらは単純に出現頻度を数えてフィルタすることで除外可能です。 -```{r} +{:.input_area} +```R text_rec %>% step_tokenfilter(surrounding_present_usage, min_times = 50) %>% prep() %>% @@ -201,42 +303,186 @@ text_rec %>% tidyr::unnest(cols = c(surrounding_present_usage)) ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 6 x 2 + ## surrounding_present_usage .row_id + ## + ## 1 低層 13534 + ## 2 が 13534 + ## 3 建ち並ぶ 13534 + ## 4 の 13534 + ## 5 良い 13534 + ## 6 地域 13534 ## TF-IDF TF-IDFは情報検索や文書推薦などで利用される特徴量の指標です。文章量が異なるデータでは、文書の長さによって出現する単語の数や回数が異なります。その影響を調整する(正規化する)働きを持ちます。 -TF-IDFはTF (索引語頻度 Term Frequency)とIDF (逆文書頻度 Inverse Document Frequency)の積より求まります。以下の式により、文書中に多く出現する単語の影響が軽減され、希少な単語の価値を高めます。 +TF-IDFはTF (索引語頻度 Term Frequency)とIDF (逆文書頻度 Inverse Document +Frequency)の積より求まります。以下の式により、文書中に多く出現する単語の影響が軽減され、希少な単語の価値を高めます。 -$$ +\[ tf-idf = tf(w,d) \times idf(w) -$$ +\] -$$ +\[ tf(w,d) = bow(w,d) / [文書d内の単語数] -$$ +\] -$$ +\[ bow(w, d) = [文書d内の単語wの出現回数] -$$ +\] - -$$ +\[ idf(w) = [全文書数N] / [単語wが含まれる文書数] -$$ +\] -```{r step_tfidf, eval=TRUE} -df_lp_kanto %>% - recipe(~ surrounding_present_usage + .row_id) %>% +{:.input_area} +```R +df_lp_kanto %>% + recipe( ~ surrounding_present_usage + .row_id) %>% step_tokenize(surrounding_present_usage) %>% -step_tfidf(surrounding_present_usage) %>% -prep() %>% -juice() + step_tfidf(surrounding_present_usage) %>% + prep() %>% + juice() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 8,476 x 510 + ## .row_id tfidf_surroundi… tfidf_surroundi… tfidf_surroundi… + ## + ## 1 13534 0 0 0 + ## 2 13604 0 0 0 + ## 3 13605 0 0 0 + ## 4 13613 0 0 0 + ## 5 13620 0 0 0 + ## 6 13621 0 0 0 + ## 7 13640 0 0 0 + ## 8 13755 0 0 0 + ## 9 13758 0 0 0 + ## 10 13765 0 0 0 + ## # … with 8,466 more rows, and 506 more variables: + ## # tfidf_surrounding_present_usage_アパ , + ## # tfidf_surrounding_present_usage_アパート , + ## # tfidf_surrounding_present_usage_あり , + ## # tfidf_surrounding_present_usage_ある , + ## # tfidf_surrounding_present_usage_い , + ## # tfidf_surrounding_present_usage_いる , + ## # tfidf_surrounding_present_usage_オフィス , + ## # tfidf_surrounding_present_usage_が , + ## # tfidf_surrounding_present_usage_ガソリンスタンド , + ## # tfidf_surrounding_present_usage_から , + ## # tfidf_surrounding_present_usage_コンビニ , + ## # tfidf_surrounding_present_usage_さ , + ## # tfidf_surrounding_present_usage_サービス , + ## # tfidf_surrounding_present_usage_し , + ## # tfidf_surrounding_present_usage_じ , + ## # tfidf_surrounding_present_usage_した , + ## # tfidf_surrounding_present_usage_スーパー , + ## # tfidf_surrounding_present_usage_する , + ## # tfidf_surrounding_present_usage_センター , + ## # tfidf_surrounding_present_usage_その , + ## # tfidf_surrounding_present_usage_た , + ## # tfidf_surrounding_present_usage_たん , + ## # tfidf_surrounding_present_usage_ち , + ## # tfidf_surrounding_present_usage_っ , + ## # tfidf_surrounding_present_usage_つ , + ## # tfidf_surrounding_present_usage_つつ , + ## # tfidf_surrounding_present_usage_って , + ## # tfidf_surrounding_present_usage_て , + ## # tfidf_surrounding_present_usage_で , + ## # tfidf_surrounding_present_usage_テラス , + ## # tfidf_surrounding_present_usage_ト , + ## # tfidf_surrounding_present_usage_と , + ## # tfidf_surrounding_present_usage_な , + ## # tfidf_surrounding_present_usage_なか , + ## # tfidf_surrounding_present_usage_なだらか , + ## # tfidf_surrounding_present_usage_など , + ## # tfidf_surrounding_present_usage_なる , + ## # tfidf_surrounding_present_usage_に , + ## # tfidf_surrounding_present_usage_において , + ## # tfidf_surrounding_present_usage_による , + ## # tfidf_surrounding_present_usage_の , + ## # tfidf_surrounding_present_usage_は , + ## # tfidf_surrounding_present_usage_ハウス , + ## # tfidf_surrounding_present_usage_ビル , + ## # tfidf_surrounding_present_usage_へ , + ## # tfidf_surrounding_present_usage_ほか , + ## # tfidf_surrounding_present_usage_ほかに , + ## # tfidf_surrounding_present_usage_ホテル , + ## # tfidf_surrounding_present_usage_まだ , + ## # tfidf_surrounding_present_usage_まれ , + ## # tfidf_surrounding_present_usage_マンション , + ## # tfidf_surrounding_present_usage_み , + ## # tfidf_surrounding_present_usage_も , + ## # tfidf_surrounding_present_usage_もみ , + ## # tfidf_surrounding_present_usage_や , + ## # tfidf_surrounding_present_usage_やや , + ## # tfidf_surrounding_present_usage_よい , + ## # tfidf_surrounding_present_usage_ら , + ## # tfidf_surrounding_present_usage_られる , + ## # tfidf_surrounding_present_usage_る , + ## # tfidf_surrounding_present_usage_れ , + ## # tfidf_surrounding_present_usage_れつ , + ## # tfidf_surrounding_present_usage_れる , + ## # tfidf_surrounding_present_usage_われ , + ## # tfidf_surrounding_present_usage_を , + ## # tfidf_surrounding_present_usage_んで , + ## # tfidf_surrounding_present_usage_以上 , + ## # tfidf_surrounding_present_usage_位 , + ## # tfidf_surrounding_present_usage_位置 , + ## # tfidf_surrounding_present_usage_囲 , + ## # tfidf_surrounding_present_usage_医院 , + ## # tfidf_surrounding_present_usage_医療 , + ## # tfidf_surrounding_present_usage_一般 , + ## # tfidf_surrounding_present_usage_一部 , + ## # tfidf_surrounding_present_usage_印刷所 , + ## # tfidf_surrounding_present_usage_飲食 , + ## # tfidf_surrounding_present_usage_飲食店 , + ## # tfidf_surrounding_present_usage_運送 , + ## # tfidf_surrounding_present_usage_営業 , + ## # tfidf_surrounding_present_usage_映画館 , + ## # tfidf_surrounding_present_usage_駅 , + ## # tfidf_surrounding_present_usage_駅前 , + ## # tfidf_surrounding_present_usage_駅前通 , + ## # tfidf_surrounding_present_usage_駅前通り , + ## # tfidf_surrounding_present_usage_駅通り , + ## # tfidf_surrounding_present_usage_園 , + ## # tfidf_surrounding_present_usage_沿い , + ## # tfidf_surrounding_present_usage_沿線 , + ## # tfidf_surrounding_present_usage_沿道 , + ## # tfidf_surrounding_present_usage_卸 , + ## # tfidf_surrounding_present_usage_卸売 , + ## # tfidf_surrounding_present_usage_温泉 , + ## # tfidf_surrounding_present_usage_化 , + ## # tfidf_surrounding_present_usage_化学 , + ## # tfidf_surrounding_present_usage_果樹 , + ## # tfidf_surrounding_present_usage_荷捌 , + ## # tfidf_surrounding_present_usage_画 , + ## # tfidf_surrounding_present_usage_介在 , + ## # tfidf_surrounding_present_usage_会館 , + ## # tfidf_surrounding_present_usage_会社 , … + ## 文字列のカウント -```{r} +{:.input_area} +```R d <- df_lp_kanto %>% sample_n(10) %>% @@ -251,32 +497,84 @@ recipe(~ ., data = d) %>% mutate(n = n()) %>% ungroup() %>% arrange(desc(n)) +``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 102 x 3 + ## .row_id surrounding_present_usage n + ## + ## 1 18254 住宅 3 + ## 2 18254 住宅 3 + ## 3 18254 住宅 3 + ## 4 19823 住宅 2 + ## 5 19823 住宅 2 + ## 6 23239 住宅 2 + ## 7 23239 住宅 2 + ## 8 21782 住宅 2 + ## 9 21782 住宅 2 + ## 10 16103 住宅 2 + ## # … with 92 more rows + +{:.input_area} +```R recipe(~ ., data = d) %>% step_count(surrounding_present_usage, pattern = "住宅", result = "count_house") %>% prep(d) %>% juice() ``` + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## Warning: All elements of `...` must be named. + ## Did you want `data = c(type, role, source)`? + + ## # A tibble: 10 x 3 + ## .row_id surrounding_present_usage count_house + ## + ## 1 19823 一般住宅、中小工場等が混在する既成住宅地域 2 + ## 2 18254 一般住宅の中に共同住宅等が散在する住宅地域 3 + ## 3 23239 一般住宅、アパート等が混在する既成住宅地域 2 + ## 4 21782 中規模一般住宅が多い区画整然とした住宅地域 2 + ## 5 15878 倉庫、工場が多い区画整理済の工業地域 0 + ## 6 23256 店舗、事務所が多い国道沿いの路線商業地域 0 + ## 7 16103 中小規模の一般住宅が多い住宅地域 2 + ## 8 17836 事務所ビル、マンション等が混在する商業地域 0 + ## 9 14842 中規模住宅が建ち並ぶ整然とした分譲住宅地域 2 + ## 10 14593 住宅、アパート等が混在し高圧線に近い住宅地域 2 + ## 語幹処理 (ステミング) 形態素解析 - + ## まとめ -- テキストの前処理で一般的な作業には限界があり、より精度の高い作業を行うにはデータに応じた辞書やリストが必要になる。 -- 扱う言語によって前処理や特徴量エンジニアリングの適用範囲が異なる + - テキストの前処理で一般的な作業には限界があり、より精度の高い作業を行うにはデータに応じた辞書やリストが必要になる。 + - 扱う言語によって前処理や特徴量エンジニアリングの適用範囲が異なる ## 関連項目 -- [次元削減](../03/dimension-reduction) + - [次元削減](../03/dimension-reduction) ## 参考文献 -- Sarah Guido and Andreas Müller (2016). Introduction to Machine Learning with Python A Guide for Data Scientists (O'Reilly) (**翻訳** 中田秀基訳 (2017). Pythonではじめる機械学習 -- 石田基広 (2017). Rによるテキストマイニング入門 第2版 (森北出版) -- Julia Silge and David Robinson (2017). [Text Mining with R A Tidy Approach](https://www.tidytextmining.com/) (O'Reilly) (**翻訳** 長尾高弘訳 (2018). Rによるテキストマイニング — tidytextを活用したデータ分析と可視化の基礎 (オライリー)) -- Benjamin Bengfort, Tony Ojeda, Rebecca Bilbro (2018). Applied Text Analysis with Python Enabling Language-Aware Data Products with Machine Learning (O'Reilly) -- Alice Zheng and Amanda Casari (2018). Feature Engineering for Machine Learning (O'Reilly) (**翻訳** 株式会社ホクソエム訳 (2019). 機械学習のための特徴量エンジニアリング (オライリー)) + - Sarah Guido and Andreas Müller (2016). Introduction to Machine + Learning with Python A Guide for Data Scientists (O’Reilly) (**翻訳** + 中田秀基訳 (2017). Pythonではじめる機械学習 + - 石田基広 (2017). Rによるテキストマイニング入門 第2版 (森北出版) + - Julia Silge and David Robinson (2017). [Text Mining with R A Tidy + Approach](https://www.tidytextmining.com/) (O’Reilly) (**翻訳** 長尾高弘訳 + (2018). Rによるテキストマイニング — tidytextを活用したデータ分析と可視化の基礎 (オライリー)) + - Benjamin Bengfort, Tony Ojeda, Rebecca Bilbro (2018). Applied Text + Analysis with Python Enabling Language-Aware Data Products with + Machine Learning (O’Reilly) + - Alice Zheng and Amanda Casari (2018). Feature Engineering for + Machine Learning (O’Reilly) (**翻訳** 株式会社ホクソエム訳 (2019). + 機械学習のための特徴量エンジニアリング (オライリー)) diff --git a/_build/images/beer_holiday_effect_comperad-1.png b/_build/images/beer_holiday_effect_comperad-1.png new file mode 100644 index 0000000000000000000000000000000000000000..0bedbe5ab85cbd096640b027db4614fb5bfe06b5 GIT binary patch literal 414368 zcmeEvc|4Te`@du>LM3Exm1HT|w`rp!lq}hXY}xmHndU(%p^_+BDw6Ek_bp`4z8gb~ zeeA~mJJXY%XDpxk)c23y>-+WJn7Q5Ooa=gD?`t{dzU~KdGLn0C)9uE?!`pM^^2Hl? zc;u9Lc)K)7h`=}X;;(G*@CXhXT(}@-bV2fhnX#Fryt$UPuB5K1uBCzY4axI(cmm$u z3hH+l6sW|qtn`l@t(G@F>tX#~e9y5xeu`&bCuhPsJNZnXU*T&zJfJf>RvlPYEx?nt z{95*sob&0SAgWJ!X?Iyp%uuw8C3E@jKGxR2azZQ_<0d!Y^=|luFwYBv%)~C;G3TjZ zbcoYfhCZ5bAmfAb^Cwh0s9(l$(46V9^7Oqg%Kq5hGmM7lwbFG)cJ(M}d_GxHyA)N# zg+eE2Yh&k1Xl$AU;{g^6=G#gA?@90b?ii0$sIcAPxbTEod`N|9_Vz&9X~-uo$8@B2 zbCH#ptG9vP*Hb<9_X!o`4etj~`c@WWB*+#|Cp1CBt{D!080dR*JIJOpEYTdTwGwJl zaK~fHq$slHMW~oNT1#z_o%a|DdHvJX4^KlSmPmSxOk7eKy4#-zJ=rTx^thwzW&FFn 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`configuration`や`fire_area`は欠損を多く含んでいることがわかります。`building_structure`にもわずかですが欠損データがあります -```{r} +```{r, eval=TRUE} # 同一のデータで重複があるもの df_lp_kanto %>% tidyr::separate_rows("current_use", sep = ",") %>% @@ -98,13 +99,13 @@ df_lp_kanto %>% ## 要約統計量の算出 -```{r} +```{r, eval=TRUE} summary(df_lp_kanto) ``` Rの組み込み関数として用意されている `summary()` を利用して、データフレームに含まれる列の要約統計量を得ることができます。 -```{r} +```{r, eval=TRUE} # 論理値型で標準偏差0の列を特定 constant cols df_lp_kanto %>% select(-starts_with(".")) %>% @@ -117,7 +118,7 @@ df_lp_kanto %>% ここでは `skimr` で行う例を示します。 -```{r} +```{r, eval=TRUE} # skimr::skim(df_lp) # Rコンソールでの実行は skim() で構いません df_lp_kanto %>% select(-starts_with(".")) %>% @@ -210,7 +211,7 @@ purrr::map( ### 全体 -```{r} +```{r, eval=TRUE} vis_dat(df_lp_kanto) ``` @@ -269,13 +270,15 @@ df_lp_kanto %>% #### 散布図 -```{r} +```{r lp_scatter_distance_from_station_posted_land_price, eval=FALSE, echo=TRUE} df_lp_kanto %>% ggplot(aes(distance_from_station, posted_land_price)) + geom_point() ``` -```{r} +![](../images/lp_scatter_distance_from_station_posted_land_price-1.png) + +```{r lp_scatter_distance_from_station_posted_land_price_logscale, eval=FALSE, echo=TRUE} df_lp_kanto %>% ggplot(aes(distance_from_station, acreage)) + geom_point() + @@ -283,6 +286,8 @@ df_lp_kanto %>% scale_y_log10() ``` +![](../images/lp_scatter_distance_from_station_posted_land_price_logscale-1.png) + ## 特殊なデータの視覚化 ### 時系列データ @@ -344,7 +349,7 @@ df_is_num %>% #### 相関行列 -```{r} +```{r, eval=TRUE} df_is_num %>% corrr::correlate() ``` @@ -356,7 +361,7 @@ df_is_num %>% ![](../images/lp_correlation-1.png) -```{r} +```{r, eval=FALSE} df_is_log %>% mutate_all(as.numeric) %>% vis_cor() diff --git a/content/01/tidy-data.Rmd b/content/01/tidy-data.Rmd index ca900c1..0eb7be6 100644 --- a/content/01/tidy-data.Rmd +++ b/content/01/tidy-data.Rmd @@ -11,6 +11,7 @@ jupyter: display_name: R language: R name: ir +output: github_document --- ```{r setup, include=FALSE} @@ -42,7 +43,7 @@ df_lp_kanto %>% `distance_from_staion` の影響が一番強く、`acreage`、`night_population`が続きます。しかし `distance_from_staion` の値が大きすぎて他の変数の効果が小さいように見えます。これは元のスケールの影響を受けているためです。 `distance_from_staion` と `acreage` の1は同じ単位ではありません。偏回帰係数を比較する場合、スケーリングを行っておく必要があります。 -```{r} +```{r, eval=TRUE} df_lp_kanto %>% recipe(posted_land_price ~ distance_from_station + acreage + night_population) %>% step_center(all_predictors()) %>% @@ -58,7 +59,7 @@ df_lp_kanto %>% [主成分分析](../03/dimension-reduction)では入力に用いる変数間のスケールが標準化されていることが前提です。それは特徴量空間におけるデータ間を距離を利用するためです。そのため、値の範囲が大きく異なる変数があることでその影響を強く受けます。まずは標準化を行わない場合の結果を見てみましょう。 -```{r} +```{r, eval=TRUE} pca_res <- prcomp(~ distance_from_station + acreage + night_population, data = df_lp_kanto, @@ -72,7 +73,7 @@ summary(pca_res) 次にあらかじめ標準化したデータを元に主成分分析を行った結果を見ます。 -```{r} +```{r, eval=TRUE} pca_res <- prcomp(~ distance_from_station + acreage + night_population, data = df_lp_kanto, @@ -185,7 +186,7 @@ $$ Min-Maxスケーリングを行う際は、数値の取り得る値があらかじめわかっているものが望ましいです。データとして存在しない値や外れ値が与えられることで、その値に引っ張られて相対的な差がなくなってしまう恐れがあるからです。 -```{r} +```{r, eval=TRUE} # 地価データの「最寄り駅までの距離」の範囲 lp_dist <- df_lp_kanto$distance_from_station range(lp_dist) @@ -200,7 +201,7 @@ range(lp_dist_minmax) 全体を考慮 -```{r} +```{r, eval=FALSE, echo=TRUE} p1 <- ggplot(df_lp_kanto, aes(x = 0, y = distance_from_station)) + geom_violin(color = ds_col(1)) @@ -237,7 +238,7 @@ $$ -```{r} +```{r, eval=FALSE, echo=TRUE} # スケーリングでは分布は変わらない range(df_lp_kanto$posted_land_price) p_base <- @@ -267,7 +268,7 @@ df_lp_kanto %>% データの表現方法を変える -```{r} +```{r, eval=TRUE} # 地価データにおける駅からの距離 # 平均と分散 df_lp_kanto %>% @@ -282,7 +283,7 @@ mean(lp_dist_scaled) # 限りなく0に近くなる sd(lp_dist_scaled) # 分散は1 ``` -```{r} +```{r, eval=TRUE} diff(range(lp_dist)) median(lp_dist) @@ -297,7 +298,7 @@ diff(range(c(scale(lp_dist)))) これらの手法は、対数変換と異なりデータの分布には影響しないことが特徴です。 -```{r} +```{r scale_histogram_comared, eval=FALSE, echo=TRUE} p1 <- ggplot(df_lp_kanto, aes(distance_from_station)) + @@ -311,6 +312,8 @@ p2 <- plot_grid(p1, p2, ncol = 2) ``` +![](../images/scale_histogram_comared-1.png) + ## データ浄化 @@ -329,7 +332,7 @@ plot_grid(p1, p2, ncol = 2) ### 欠損処理 -![欠損処理](../03/handling-missing-data) +[欠損処理](../03/handling-missing-data) ### 不要な列の削除 @@ -353,7 +356,7 @@ plot_grid(p1, p2, ncol = 2) - データセット全体 - 完全に欠損する行・列 -```{r} +```{r, eval=TRUE} df_lp_kanto %>% skimr::skim_to_list() ``` @@ -364,11 +367,12 @@ df_lp_kanto %>% skimr::skim_to_list() 数値データでは稀ですが、論理値変数やカテゴリ変数に潜んでいる、こうした1種類の値しか取らないデータは除外しましょう。 -```{r} +```{r, eval=TRUE} +# 分散ゼロ scale(c(0, 0, 0), scale = TRUE) ``` -```{r} +```{r, eval=TRUE} df_lp_kanto_clean <- df_lp_kanto %>% verify(ncol(.) == 45) %>% @@ -386,7 +390,7 @@ dim(df_lp_kanto_clean) どうやら違うようです。 -```{r} +```{r, eval=TRUE} df_lp_kanto %>% distinct(attribute_change_urban_planning_area, attribute_change_use_district) @@ -396,7 +400,7 @@ df_lp_kanto %>% 変数の数は減りますが、重要な変数を削除してしまう恐れがあります。この作業は慎重に -```{r} +```{r, eval=TRUE} dim(df_lp_kanto_clean) nzv_filter <- @@ -437,7 +441,17 @@ df_lp_kanto_clean <- filtered_te いずれかの変数だけを利用するようにしましょう。 -```{r} +```{r, eval=TRUE} +library(GGally) +df_lp_kanto_clean %>% + select(-starts_with(".")) %>% + select_if(is.numeric) %>% + corrr::correlate() +``` + +```{r lp_correlation_ggally, eval=FALSE, echo=TRUE} +library(GGally) + df_lp_kanto_clean %>% select(-starts_with(".")) %>% select_if(is.numeric) %>% @@ -448,16 +462,12 @@ df_lp_kanto_clean %>% select(-starts_with(".")) %>% select_if(is.numeric) %>% ggcorr(label = TRUE, label_round = 2) - -df_hazard_kys %>% - select(-block_no, -longitude, -latitude) %>% - select_if(is.numeric) %>% - ggcorr(label = TRUE, label_round = 2) ``` -```{r} -# df_lp_kanto では不適切 +![](../images/lp_correlation_ggally-1.png) +```{r, eval=TRUE} +# df_lp_kanto では不適切 df_lp_kanto_clean %>% recipe(~ .) %>% step_corr(all_numeric(), -starts_with("."), threshold = 0.9) %>% diff --git a/content/01/tidymodels-workflow.Rmd b/content/01/tidymodels-workflow.Rmd index ab1a01a..b98aff7 100644 --- a/content/01/tidymodels-workflow.Rmd +++ b/content/01/tidymodels-workflow.Rmd @@ -11,6 +11,7 @@ jupyter: display_name: R language: R name: ir +output: github_document --- ```{r setup, include=FALSE, results="hide"} diff --git a/content/02/categorical.Rmd b/content/02/categorical.Rmd index cd3bca3..bb7e561 100644 --- a/content/02/categorical.Rmd +++ b/content/02/categorical.Rmd @@ -11,6 +11,7 @@ jupyter: display_name: R language: R name: ir +output: github_document --- ```{r setup, include=FALSE, eval=TRUE, results="hide"} @@ -193,7 +194,7 @@ dummy_lm_res effectコーディングでは、切片はターゲットとなる変数の全体平均を表し、係数は各カテゴリの平均値と全体平均との差分を表します。このことにより、各カテゴリの効果 (main effect) を主眼に捉えることができます。なお参照カテゴリの係数を算出するにはカテゴリの係数を合計してマイナスをつけます。 -```{r} +```{r, eval=TRUE} -sum(dummy_lm_res$estimate[-1]) ``` @@ -239,7 +240,7 @@ df_hazard %>% この出現頻度が低いメッシュコードをまとめてみましょう。今回は全体で10未満の出現頻度のメッシュコードを稀なカテゴリとして扱います。これにより、`meshCode`は28の水準を含むカテゴリになりました。この数が多いか少ないかは議論が残るところですが、ダミー変数化による分散ゼロの変数が含まれる確率は低くなったでしょう。 -```{r} +```{r, eval=TRUE} df_hazard %>% recipe(~ .) %>% step_other(meshCode, other = "other_meshcode", threshold = 0.0021) %>% # 0.0004 @@ -347,7 +348,7 @@ XOR ビールの支出データに含まれるweatherdaytime_06_00_18_00には、「晴」や「曇」だけでなく「曇一時雨」や「雨後時々曇」といった気象に関する項目が含まれます。項目の組み合わせによる表現が可能であるため、カテゴリの数は多くなっています。 -```{r} +```{r, eval=TRUE} df_beer2018q2 %>% count(weatherdaytime_06_00_18_00) ``` @@ -356,7 +357,7 @@ df_beer2018q2 %>% を新たな特徴量として活用するのがexpansion encodingになります。 -```{r} +```{r, eval=TRUE} df_beer2018q2_baked <- df_beer2018q2 %>% select(date, expense, weatherdaytime_06_00_18_00) %>% @@ -371,7 +372,7 @@ df_beer2018q2_baked 複雑なカテゴリを評価するのではなく、大雑把なカテゴリとして扱いたい場合にはカテゴリの項目を減らすことが有効でしょう。 -```{r} +```{r, eval=TRUE} df_beer2018q2_baked %>% count(weatherdaytime_06_00_18_00_main, weatherdaytime_06_00_18_00_sub) @@ -400,7 +401,7 @@ Expansion encodingを実行した時は、不用意に分散ゼロの特徴量 2次 中 = 0.41, 大 = -0.82, 小=0.41 -```{r} +```{r, eval=FALSE} df_lp_kanto %>% count(current_use) %>% ggplot(aes(forcats::fct_reorder(current_use, n), n)) + diff --git a/content/02/date-and-time.Rmd b/content/02/date-and-time.Rmd index f6a5c84..d03b380 100644 --- a/content/02/date-and-time.Rmd +++ b/content/02/date-and-time.Rmd @@ -11,6 +11,7 @@ jupyter: display_name: R language: R name: ir +output: github_document --- ```{r setup, include=FALSE, eval=TRUE, results="hide"} @@ -86,7 +87,7 @@ glimpse(df_baked_split_date) 日付を記録するdate列の要素が分解され、新たな特徴量として追加されました。それでは曜日のダミーコーディングを実行します。 -```{r} +```{r, eval=TRUE} df_baked_split_date %>% recipe(expense ~ .) %>% step_dummy(date_dow) %>% @@ -109,16 +110,7 @@ df_beer_prep <- df_beer_prep ``` -```{r} -df_baked_split_date %>% - recipe(expense ~ .) %>% - step_dummy(date_dow, one_hot = TRUE) %>% - prep(training = df_baked_split_date) %>% - bake(new_data = df_baked_split_date) %>% - select(starts_with("date_dow"), everything()) -``` - -```{r} +```{r, eval=TRUE} # 日本の祝日判定に次の項目が利用可能 stringr::str_subset(timeDate::listHolidays(), "^JP") ``` @@ -132,7 +124,7 @@ stringr::str_subset(timeDate::listHolidays(), "^JP") 今度は分解した曜日の情報をもとに、ビールの売り上げは「翌日に仕事が控えている曜日よりも休日の方が増えそうだ」という直感を調べてみましょう。 -```{r} +```{r, eval=TRUE} df_baked_split_date <- df_baked_split_date %>% mutate(is_weekend = if_else(date_dow %in% c("土", "日"), @@ -140,7 +132,7 @@ df_baked_split_date <- FALSE)) ``` -```{r} +```{r beer_holiday_effect_comperad, eval=FALSE, echo=TRUE} df_baked_split_date %>% ggplot(aes(date_dow, expense)) + geom_boxplot(aes(color = is_weekend), outlier.shape = NA) + @@ -150,6 +142,8 @@ df_baked_split_date %>% facet_wrap(~ date_month) ``` +![](../images/beer_holiday_effect_comperad-1.png) + 目論見通り、どの月でも平日よりも休日の方が支出が増えているようです。また、8月は週ごとに変動が大きく、9月では平日との差がほとんどないということもグラフから読み取れます。新たな特徴量の作成と関係の図示により、経験的な推論を確認するだけでなく、モデルに対する新たな洞察も得ることができました。 7月から9月にかけては @@ -161,7 +155,7 @@ df_baked_split_date %>% が祝日となります。また、この期間で考慮すべき日付として「お盆」の期間があります。多くの企業でこの期間は夏休みとなっていると考えられます。8月13日から15日のデータも他の日付と区別できるようにしておきます。 -```{r} +```{r, eval=TRUE} df_beer_prep <- df_beer_prep %>% recipe(expense ~ .) %>% @@ -179,7 +173,7 @@ df_beer_prep 平日であれば1を与える列を追加します。 -```{r} +```{r, eval=TRUE} df_beer_prep <- df_beer_prep %>% mutate(is_weekday = if_else(date_dow %in% c("土", "日"), @@ -195,7 +189,7 @@ df_beer_prep <- df_beer_prep ``` -```{r} +```{r, eval=TRUE} df_beer_baked <- df_beer_prep %>% recipe(expense ~ .) %>% @@ -210,7 +204,7 @@ df_beer_baked <- 移動平均は対象時点のデータとその前後の値を足して平均を取ることで計算されます。前後の値をいくつ取るか、これは窓枠の数 (ウィンドウ window size)と呼ばれ、あらかじめ決める必要があります。ウィンドウの数は重要で、大きすぎると平滑化により非線形の関係が消えてしまう恐れがあります。例えばウィンドウを3とした時は自身と前後の一つずつの値から値が求まります。この場合では同じ値が使われることは少ないですが、大きなウィンドウを設定すると別々のデータで同じデータが使われることになるため、個別のデータがより平均に近づきます。 -```{r} +```{r beer_ts_window, eval=FALSE, echo=TRUE} p1 <- df_beer2018q2 %>% ggplot(aes(date, expense)) + geom_point(color = ds_col(1)) @@ -226,6 +220,8 @@ p2 <- df_beer2018q2 %>% plot_grid(p1, p2, ncol = 1) ``` +![](../images/beer_ts_window-1.png) + 短期間の中央値を利用することも(異常値がある場合) 異常値を含むように窓枠の数を決める @@ -246,7 +242,7 @@ plot_grid(p1, p2, ncol = 1) sin, cos変換 -```{r} +```{r step_hyperbolic, eval=FALSE, echo=TRUE} set.seed(313) examples <- matrix(rnorm(40), ncol = 2) examples <- as.data.frame(examples) @@ -262,16 +258,16 @@ cos_obj <- prep(cos_trans, training = examples) transformed_te <- bake(cos_obj, examples) plot(examples$V1, transformed_te$V1) - -plot(sin(1:12), cos(1:12)) ``` +![](../images/step_hyperbolic-1.png) + ## 特定の時点からの経過 期間内のある瞬間の情報 特定のイベントからの経過時間 -```{r} +```{r, eval=TRUE} n <- 10 start <- as.Date('1999/01/01') end <- as.Date('1999/01/10') diff --git a/content/02/numeric.Rmd b/content/02/numeric.Rmd index 49ca85f..6649692 100644 --- a/content/02/numeric.Rmd +++ b/content/02/numeric.Rmd @@ -11,6 +11,7 @@ jupyter: display_name: R language: R name: ir +output: github_document --- ```{r setup, include=FALSE, results="hide"} @@ -427,7 +428,7 @@ df %>% - 中距離... 800m~2km - 遠距離... 2km以上 -```{r} +```{r, eval=TRUE} df_lp_kanto$distance_from_station %>% summary() df_lp_kanto_dist_c <- @@ -439,7 +440,9 @@ df_lp_kanto_dist_c <- distance_from_station > 2000 ~ "遠距離" ) %>% forcats::fct_inorder()) +``` +```{r lp_categorize_distance, eval=FALSE, echo=TRUE} df_lp_kanto_dist_c %>% group_by(distance_cat) %>% summarise(posted_land_price = mean(posted_land_price)) %>% @@ -447,11 +450,13 @@ df_lp_kanto_dist_c %>% geom_bar(stat = "identity") ``` +![](../images/lp_categorize_distance-1.png) + ### 分位数による離散化 - 均等に分割 -```{r} +```{r, eval=TRUE} # 5段階の階級を設定 binned <- discretize(df_lp_kanto$distance_from_station, @@ -462,7 +467,7 @@ binned <- table(predict(binned, df_lp_kanto$distance_from_station)) ``` -```{r} +```{r, eval=TRUE} df_lp_kanto %>% recipe(~ distance_from_station) %>% step_discretize(distance_from_station, @@ -470,9 +475,9 @@ df_lp_kanto %>% prep() %>% juice() -df_lp_kanto %>% - ggplot(aes(distance_from_station, posted_land_price)) + - geom_point() +# df_lp_kanto %>% +# ggplot(aes(distance_from_station, posted_land_price)) + +# geom_point() ``` @@ -491,7 +496,7 @@ df_lp_kanto %>% ビールの売り上げについて気温と湿度の交互作用を考えてみましょう。これは気温がビールの売り上げに及ぼす影響を湿度が調整すると考えるものです。 -```{r} +```{r lp_interactions, eval=FALSE, echo=TRUE} df_lp_kanto %>% group_by(urban_planning_area, gas_facility) %>% summarise(posted_land_price_grp = mean(posted_land_price)) %>% @@ -501,7 +506,9 @@ df_lp_kanto %>% geom_point() ``` -```{r} +![](../images/lp_interactions-1.png) + +```{r, eval = TRUE} df_beer2018q2 %>% recipe(expense ~ temperature_average +humidity_average_percent) %>% step_interact(terms = ~ temperature_average:humidity_average_percent) %>% diff --git a/content/02/spatial-data.Rmd b/content/02/spatial-data.Rmd index ce8fb76..0ee6ffe 100644 --- a/content/02/spatial-data.Rmd +++ b/content/02/spatial-data.Rmd @@ -11,6 +11,7 @@ jupyter: display_name: R language: R name: ir +output: github_document --- ```{r setup, include=FALSE, eval=TRUE, results="hide"} @@ -114,7 +115,7 @@ source(here::here("R/prepared_landprice.R")) 土砂災害・雪崩メッシュデータに含まれる メッシュコード (`meshCode`) は桁数が7です。つまり5kmの範囲をカバーするメッシュコードだということがわかります。 -```{r} +```{r, eval=TRUE} df_hazard %>% select(meshCode) ``` @@ -190,13 +191,9 @@ ggplot() + 乗車人数を加えてみることにします。 -```{r} -df_lp_kanto -``` - -### 参照座標系が異なるために生じる問題 +### 参照座標系が異なるために生じる問題 ## まとめ diff --git a/content/02/text.Rmd b/content/02/text.Rmd index f5c46e7..a0288e7 100644 --- a/content/02/text.Rmd +++ b/content/02/text.Rmd @@ -11,6 +11,7 @@ jupyter: display_name: R language: R name: ir +output: github_document --- ```{r setup, include=FALSE, eval=TRUE, results="hide"} @@ -119,7 +120,7 @@ df_lp_prep %>% ```{r, eval= TRUE} x <- na.omit(df_lp_kanto$surrounding_present_usage)[1] -tokenizer +tokenizers::tokenize_words(x) ``` ## Bag-of-Words @@ -187,7 +188,7 @@ df_lp_kanto %>% 出現頻度の少ない単語も解析の際に障害となる可能性があります。これらは単純に出現頻度を数えてフィルタすることで除外可能です。 -```{r} +```{r, eval=TRUE} text_rec %>% step_tokenfilter(surrounding_present_usage, min_times = 50) %>% prep() %>% @@ -221,17 +222,17 @@ idf(w) = [全文書数N] / [単語wが含まれる文書数] $$ ```{r step_tfidf, eval=TRUE} -df_lp_kanto %>% - recipe(~ surrounding_present_usage + .row_id) %>% +df_lp_kanto %>% + recipe( ~ surrounding_present_usage + .row_id) %>% step_tokenize(surrounding_present_usage) %>% -step_tfidf(surrounding_present_usage) %>% -prep() %>% -juice() + step_tfidf(surrounding_present_usage) %>% + prep() %>% + juice() ``` ## 文字列のカウント -```{r} +```{r, eval=TRUE} d <- df_lp_kanto %>% sample_n(10) %>%

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