diff --git a/COVID.ipynb b/COVID.ipynb index 51b86bd..751aa9c 100644 --- a/COVID.ipynb +++ b/COVID.ipynb @@ -21,157 +21,172 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

5 rows × 10 columns (omitted printing of 1 columns)

DateRepDayMonthYearNewConfCasesDeathsCountryExpGeoIdCountry Code
StringInt64Int64Int64Int64Int64StringStringString⍰
13/26/20202632020330AfghanistanAFAFG
23/25/2020253202020AfghanistanAFAFG
33/24/2020243202061AfghanistanAFAFG
43/23/20202332020100AfghanistanAFAFG
53/22/2020223202000AfghanistanAFAFG
" + "

5 rows × 10 columns (omitted printing of 2 columns)

dateRepdaymonthyearcasesdeathscountriesAndTerritoriesgeoId
StringInt64Int64Int64Int64Int64StringString
130/03/2020303202081AfghanistanAF
229/03/20202932020151AfghanistanAF
328/03/20202832020161AfghanistanAF
427/03/2020273202000AfghanistanAF
526/03/20202632020330AfghanistanAF
" ], "text/latex": [ - "\\begin{tabular}{r|cccccccccc}\n", - "\t& DateRep & Day & Month & Year & NewConfCases & Deaths & CountryExp & GeoId & Country Code & \\\\\n", + "\\begin{tabular}{r|ccccccccc}\n", + "\t& dateRep & day & month & year & cases & deaths & countriesAndTerritories & geoId & \\\\\n", "\t\\hline\n", - "\t& String & Int64 & Int64 & Int64 & Int64 & Int64 & String & String & String⍰ & \\\\\n", + "\t& String & Int64 & Int64 & Int64 & Int64 & Int64 & String & String & \\\\\n", "\t\\hline\n", - "\t1 & 3/26/2020 & 26 & 3 & 2020 & 33 & 0 & Afghanistan & AF & AFG & $\\dots$ \\\\\n", - "\t2 & 3/25/2020 & 25 & 3 & 2020 & 2 & 0 & Afghanistan & AF & AFG & $\\dots$ \\\\\n", - "\t3 & 3/24/2020 & 24 & 3 & 2020 & 6 & 1 & Afghanistan & AF & AFG & $\\dots$ \\\\\n", - "\t4 & 3/23/2020 & 23 & 3 & 2020 & 10 & 0 & Afghanistan & AF & AFG & $\\dots$ \\\\\n", - "\t5 & 3/22/2020 & 22 & 3 & 2020 & 0 & 0 & Afghanistan & AF & AFG & $\\dots$ \\\\\n", + "\t1 & 30/03/2020 & 30 & 3 & 2020 & 8 & 1 & Afghanistan & AF & $\\dots$ \\\\\n", + "\t2 & 29/03/2020 & 29 & 3 & 2020 & 15 & 1 & Afghanistan & AF & $\\dots$ \\\\\n", + "\t3 & 28/03/2020 & 28 & 3 & 2020 & 16 & 1 & Afghanistan & AF & $\\dots$ \\\\\n", + "\t4 & 27/03/2020 & 27 & 3 & 2020 & 0 & 0 & Afghanistan & AF & $\\dots$ \\\\\n", + "\t5 & 26/03/2020 & 26 & 3 & 2020 & 33 & 0 & Afghanistan & AF & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "5×10 DataFrame. Omitted printing of 4 columns\n", - "│ Row │ DateRep │ Day │ Month │ Year │ NewConfCases │ Deaths │\n", - "│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │\n", - "├─────┼───────────┼───────┼───────┼───────┼──────────────┼────────┤\n", - "│ 1 │ 3/26/2020 │ 26 │ 3 │ 2020 │ 33 │ 0 │\n", - "│ 2 │ 3/25/2020 │ 25 │ 3 │ 2020 │ 2 │ 0 │\n", - "│ 3 │ 3/24/2020 │ 24 │ 3 │ 2020 │ 6 │ 1 │\n", - "│ 4 │ 3/23/2020 │ 23 │ 3 │ 2020 │ 10 │ 0 │\n", - "│ 5 │ 3/22/2020 │ 22 │ 3 │ 2020 │ 0 │ 0 │" + "│ Row │ dateRep │ day │ month │ year │ cases │ deaths │\n", + "│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │\n", + "├─────┼────────────┼───────┼───────┼───────┼───────┼────────┤\n", + "│ 1 │ 30/03/2020 │ 30 │ 3 │ 2020 │ 8 │ 1 │\n", + "│ 2 │ 29/03/2020 │ 29 │ 3 │ 2020 │ 15 │ 1 │\n", + "│ 3 │ 28/03/2020 │ 28 │ 3 │ 2020 │ 16 │ 1 │\n", + "│ 4 │ 27/03/2020 │ 27 │ 3 │ 2020 │ 0 │ 0 │\n", + "│ 5 │ 26/03/2020 │ 26 │ 3 │ 2020 │ 33 │ 0 │" ] }, - "execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "download(\"https://opendata.ecdc.europa.eu/covid19/casedistribution/csv\", \"covid.csv\")\n", "data = CSV.read(\"covid.csv\")\n", - "rename!(data, Dict(\n", - " Symbol(\"Countries and territories\") => \"CountryExp\",\n", - " :Cases => \"NewConfCases\"\n", - " ))\n", "first(data, 5)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "┌ Warning: `getindex(df::DataFrame, col_ind::ColumnIndex)` is deprecated, use `df[!, col_ind]` instead.\n", - "│ caller = top-level scope at In[4]:1\n", - "└ @ Core In[4]:1\n" + "ename": "ArgumentError", + "evalue": "ArgumentError: Tried renaming :countriesAndTerritories to :CountryExp, when :countriesAndTerritories does not exist in the Index.", + "output_type": "error", + "traceback": [ + "ArgumentError: Tried renaming :countriesAndTerritories to :CountryExp, when :countriesAndTerritories does not exist in the Index.", + "", + "Stacktrace:", + " [1] rename!(::DataFrames.Index, ::Array{Pair{Symbol,Symbol},1}) at /home/abel/.julia/packages/DataFrames/uPgZV/src/other/index.jl:70", + " [2] rename!(::DataFrame, ::Dict{Symbol,String}) at /home/abel/.julia/packages/DataFrames/uPgZV/src/abstractdataframe/abstractdataframe.jl:170", + " [3] top-level scope at In[7]:1" ] - }, + } + ], + "source": [ + "rename!(data, Dict(\n", + " Symbol(\"countriesAndTerritories\") => \"CountryExp\",\n", + " :cases => \"NewConfCases\"\n", + " ))\n", + "first(data, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ { "data": { "text/html": [ - "

84 rows × 10 columns (omitted printing of 1 columns)

DateRepDayMonthYearNewConfCasesDeathsCountryExpGeoIdCountry Code
StringInt64Int64Int64Int64Int64StringStringString⍰
13/26/2020263202023211BrazilBRBRA
23/25/2020253202031012BrazilBRBRA
33/24/202024320203459BrazilBRBRA
43/23/202023320204187BrazilBRBRA
53/22/202022320202247BrazilBRBRA
63/21/202021320202835BrazilBRBRA
73/20/202020320201932BrazilBRBRA
83/19/202019320201373BrazilBRBRA
93/18/20201832020571BrazilBRBRA
103/17/20201732020340BrazilBRBRA
113/16/20201632020790BrazilBRBRA
123/15/20201532020230BrazilBRBRA
133/14/20201432020210BrazilBRBRA
143/13/20201332020250BrazilBRBRA
153/12/20201232020180BrazilBRBRA
163/11/2020113202090BrazilBRBRA
173/9/2020932020120BrazilBRBRA
183/8/202083202000BrazilBRBRA
193/7/202073202050BrazilBRBRA
203/6/202063202050BrazilBRBRA
213/5/202053202010BrazilBRBRA
223/2/202023202000BrazilBRBRA
233/1/202013202010BrazilBRBRA
242/29/2020292202000BrazilBRBRA
252/28/2020282202000BrazilBRBRA
262/27/2020272202000BrazilBRBRA
272/26/2020262202010BrazilBRBRA
282/25/2020252202000BrazilBRBRA
292/24/2020242202000BrazilBRBRA
302/23/2020232202000BrazilBRBRA
" + "

88 rows × 10 columns (omitted printing of 1 columns)

dateRepdaymonthyearNewConfCasesdeathsCountryExpgeoIdcountryterritoryCode
StringInt64Int64Int64Int64Int64StringStringString⍰
130/03/2020303202035222BrazilBRBRA
229/03/2020293202048722BrazilBRBRA
328/03/2020283202050215BrazilBRBRA
427/03/2020273202048220BrazilBRBRA
526/03/2020263202023211BrazilBRBRA
625/03/2020253202031012BrazilBRBRA
724/03/202024320203459BrazilBRBRA
823/03/202023320204187BrazilBRBRA
922/03/202022320202247BrazilBRBRA
1021/03/202021320202835BrazilBRBRA
1120/03/202020320201932BrazilBRBRA
1219/03/202019320201373BrazilBRBRA
1318/03/20201832020571BrazilBRBRA
1417/03/20201732020340BrazilBRBRA
1516/03/20201632020790BrazilBRBRA
1615/03/20201532020230BrazilBRBRA
1714/03/20201432020210BrazilBRBRA
1813/03/20201332020250BrazilBRBRA
1912/03/20201232020180BrazilBRBRA
2011/03/2020113202090BrazilBRBRA
2109/03/2020932020120BrazilBRBRA
2208/03/202083202000BrazilBRBRA
2307/03/202073202050BrazilBRBRA
2406/03/202063202050BrazilBRBRA
2505/03/202053202010BrazilBRBRA
2602/03/202023202000BrazilBRBRA
2701/03/202013202010BrazilBRBRA
2829/02/2020292202000BrazilBRBRA
2928/02/2020282202000BrazilBRBRA
3027/02/2020272202000BrazilBRBRA
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"84×10 DataFrame. Omitted printing of 4 columns\n", - "│ Row │ DateRep │ Day │ Month │ Year │ NewConfCases │ Deaths │\n", + "88×10 DataFrame. Omitted printing of 4 columns\n", + "│ Row │ dateRep │ day │ month │ year │ NewConfCases │ deaths │\n", "│ │ \u001b[90mString\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mInt64\u001b[39m │\n", "├─────┼────────────┼───────┼───────┼───────┼──────────────┼────────┤\n", - "│ 1 │ 3/26/2020 │ 26 │ 3 │ 2020 │ 232 │ 11 │\n", - "│ 2 │ 3/25/2020 │ 25 │ 3 │ 2020 │ 310 │ 12 │\n", - "│ 3 │ 3/24/2020 │ 24 │ 3 │ 2020 │ 345 │ 9 │\n", - "│ 4 │ 3/23/2020 │ 23 │ 3 │ 2020 │ 418 │ 7 │\n", - "│ 5 │ 3/22/2020 │ 22 │ 3 │ 2020 │ 224 │ 7 │\n", - "│ 6 │ 3/21/2020 │ 21 │ 3 │ 2020 │ 283 │ 5 │\n", - "│ 7 │ 3/20/2020 │ 20 │ 3 │ 2020 │ 193 │ 2 │\n", - "│ 8 │ 3/19/2020 │ 19 │ 3 │ 2020 │ 137 │ 3 │\n", - "│ 9 │ 3/18/2020 │ 18 │ 3 │ 2020 │ 57 │ 1 │\n", - "│ 10 │ 3/17/2020 │ 17 │ 3 │ 2020 │ 34 │ 0 │\n", + "│ 1 │ 30/03/2020 │ 30 │ 3 │ 2020 │ 352 │ 22 │\n", + "│ 2 │ 29/03/2020 │ 29 │ 3 │ 2020 │ 487 │ 22 │\n", + "│ 3 │ 28/03/2020 │ 28 │ 3 │ 2020 │ 502 │ 15 │\n", + "│ 4 │ 27/03/2020 │ 27 │ 3 │ 2020 │ 482 │ 20 │\n", + "│ 5 │ 26/03/2020 │ 26 │ 3 │ 2020 │ 232 │ 11 │\n", + "│ 6 │ 25/03/2020 │ 25 │ 3 │ 2020 │ 310 │ 12 │\n", + "│ 7 │ 24/03/2020 │ 24 │ 3 │ 2020 │ 345 │ 9 │\n", + "│ 8 │ 23/03/2020 │ 23 │ 3 │ 2020 │ 418 │ 7 │\n", + "│ 9 │ 22/03/2020 │ 22 │ 3 │ 2020 │ 224 │ 7 │\n", + "│ 10 │ 21/03/2020 │ 21 │ 3 │ 2020 │ 283 │ 5 │\n", "⋮\n", - "│ 74 │ 1/10/2020 │ 10 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 75 │ 1/9/2020 │ 9 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 76 │ 1/8/2020 │ 8 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 77 │ 1/7/2020 │ 7 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 78 │ 1/6/2020 │ 6 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 79 │ 1/5/2020 │ 5 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 80 │ 1/4/2020 │ 4 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 81 │ 1/3/2020 │ 3 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 82 │ 1/2/2020 │ 2 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 83 │ 1/1/2020 │ 1 │ 1 │ 2020 │ 0 │ 0 │\n", - "│ 84 │ 12/31/2019 │ 31 │ 12 │ 2019 │ 0 │ 0 │" + "│ 78 │ 10/01/2020 │ 10 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 79 │ 09/01/2020 │ 9 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 80 │ 08/01/2020 │ 8 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 81 │ 07/01/2020 │ 7 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 82 │ 06/01/2020 │ 6 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 83 │ 05/01/2020 │ 5 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 84 │ 04/01/2020 │ 4 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 85 │ 03/01/2020 │ 3 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 86 │ 02/01/2020 │ 2 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 87 │ 01/01/2020 │ 1 │ 1 │ 2020 │ 0 │ 0 │\n", + "│ 88 │ 31/12/2019 │ 31 │ 12 │ 2019 │ 0 │ 0 │" ] }, - "execution_count": 4, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df = data[findall(data[:CountryExp] .== \"Brazil\"),:]\n", + "df = data[findall(data.CountryExp .== \"Brazil\"),:]\n", "df" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -189,208 +204,223 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", - "\n", + "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "5\n", + "\n", + "\n", + "5\n", "\n", - "\n", - "10\n", + "\n", + "10\n", "\n", - "\n", - "15\n", + "\n", + "15\n", "\n", - 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"execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter(y[findfirst(y .> 0):end])" + "scatter(y[findfirst(y .> 0):end], leg=false)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "27-element Array{Int64,1}:\n", + "31-element Array{Int64,1}:\n", " 1\n", " 0\n", " 0\n", @@ -405,10 +435,6 @@ " 9\n", " 18\n", " ⋮\n", - " 23\n", - " 79\n", - " 34\n", - " 57\n", " 137\n", " 193\n", " 283\n", @@ -416,10 +442,14 @@ " 418\n", " 345\n", " 310\n", - " 232" + " 232\n", + " 482\n", + " 502\n", + " 487\n", + " 352" ] }, - "execution_count": 7, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -431,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -440,180 +470,206 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", - "\n", + "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", - "\n", + "\n", - "\n", - "\n", - "\n", - "\n", - "5\n", + "\n", + "5\n", "\n", - "\n", - "10\n", + "\n", + "10\n", "\n", - "\n", - "15\n", + "\n", + "15\n", "\n", - "\n", - "20\n", + "\n", + "20\n", "\n", - "\n", - "25\n", + "\n", + "25\n", "\n", - "\n", + "\n", + "30\n", + "\n", + "\n", "0\n", "\n", - "\n", - "100\n", - "\n", - "\n", - "200\n", - "\n", - "\n", - "300\n", - "\n", - "\n", - "400\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "100\n", + "\n", + "\n", + "200\n", + "\n", + "\n", + "300\n", + "\n", + "\n", + "400\n", + "\n", + "\n", + "500\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -622,6 +678,201 @@ "scatter(y, ms=3, leg=false)" ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "\n", + "6\n", + "\n", + "\n", + "9\n", + "\n", + "\n", + "12\n", + "\n", + "\n", + "15\n", + "\n", + "\n", + "0\n", + "\n", + "\n", + "5.0×10\n", + "\n", + "\n", + "3\n", + "\n", + "\n", + "1.0×10\n", + "\n", + "\n", + "4\n", + "\n", + "\n", + "1.5×10\n", + "\n", + "\n", + "4\n", + "\n", + "\n", + "2.0×10\n", + "\n", + "\n", + "4\n", + "\n", + "\n", + "2.5×10\n", + "\n", + "\n", + "4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter(cumsum(y), ms=3, leg=false)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -631,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -640,163 +891,163 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", "0.00\n", "\n", - "\n", + "\n", "0.25\n", "\n", - "\n", + "\n", "0.50\n", "\n", - "\n", + "\n", "0.75\n", "\n", - "\n", + "\n", "1.00\n", "\n", - "\n", + "\n", "3.0\n", "\n", - "\n", + "\n", "3.5\n", "\n", - "\n", + "\n", "4.0\n", "\n", - "\n", + "\n", "4.5\n", "\n", - "\n", + "\n", "5.0\n", "\n", - "\n", + "\n", "5.5\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", - "\n", "\n" ] }, - "execution_count": 9, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -820,7 +1071,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -829,134 +1080,134 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", "0.00\n", "\n", - "\n", + "\n", "0.25\n", "\n", - "\n", + "\n", "0.50\n", "\n", - "\n", + "\n", "0.75\n", "\n", - "\n", + "\n", "1.00\n", "\n", - "\n", + "\n", "1.5\n", "\n", - "\n", + "\n", "2.0\n", "\n", - "\n", + "\n", "2.5\n", "\n", - "\n", + "\n", "3.0\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1007,7 +1258,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1015,8 +1266,8 @@ "output_type": "stream", "text": [ "┌ Warning: `getindex(df::DataFrame, col_ind::ColumnIndex)` is deprecated, use `df[!, col_ind]` instead.\n", - "│ caller = top-level scope at In[17]:1\n", - "└ @ Core In[17]:1\n" + "│ caller = top-level scope at In[28]:1\n", + "└ @ Core In[28]:1\n" ] }, { @@ -1025,416 +1276,306 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", - " \n", + " \n", + " \n", " \n", "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "\n", - "20\n", - "\n", - "\n", - "40\n", - "\n", - "\n", - "60\n", - "\n", - "\n", - "0\n", - "\n", - "\n", - "1×10\n", - "\n", - "\n", - "5\n", - "\n", - "\n", - "2×10\n", - "\n", - "\n", - "5\n", - "\n", - "\n", - "3×10\n", - "\n", - "\n", - "5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "20\n", + "\n", + "\n", + "30\n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "2.5 \n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "3.0 \n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "3.5 \n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "4.0 \n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "4.5 \n", + "\n", + "\n", + "10\n", + "\n", + "\n", + "5.0 \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n" ] }, - "execution_count": 17, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df = data[findall(data[:CountryExp] .== \"China\"),:]\n", + "df = data[findall(data[:CountryExp] .== \"Italy\"),:]\n", "y = reverse(df.NewConfCases);\n", "y = cumsum(y)\n", "y = y[findfirst(y .>= 100):end]\n", "\n", - "# A partir de quando Δy > 0\n", - "#y = y[findlast(y .== 0)+1:end]\n", - "\n", - "#y = cumsum(y[findlast(y .== 0)+1:end])\n", - "#y = cumsum(y[findfirst(y .> 0):end])\n", - "\n", "n = length(y)\n", "x = range(1, n, length=n)\n", "\n", @@ -1452,7 +1593,7 @@ "#xg = range(1, n, length=n)\n", "X = [ones(length(xg)) xg]\n", "plot!(xg, exp.(X * β), c=:red, lw=2)\n", - "scatter!(xg, exp.(X * β), c=:red, lw=2)" + "scatter!(xg, exp.(X * β), c=:red, lw=2, yaxis=:log)" ] }, { @@ -1464,38 +1605,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "┌ Info: Precompiling NLPModelsIpopt [f4238b75-b362-5c4c-b852-0801c9a21d71]\n", - "└ @ Base loading.jl:1273\n" - ] - } - ], + "outputs": [], "source": [ "using NLPModels, NLPModelsIpopt" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 119, "metadata": { "scrolled": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "┌ Warning: `getindex(df::DataFrame, col_ind::ColumnIndex)` is deprecated, use `df[!, col_ind]` instead.\n", - "│ caller = top-level scope at In[31]:1\n", - "└ @ Core In[31]:1\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -1518,69 +1641,63 @@ " inequality constraints with only upper bounds: 0\n", "\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 0 9.7911950e+06 0.00e+00 9.95e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", - " 1 9.7909130e+06 0.00e+00 1.01e+02 -1.0 1.31e+00 2.0 1.00e+00 3.55e-02f 1\n", - " 2 9.7885337e+06 0.00e+00 1.03e+02 -1.0 3.85e-01 2.4 1.00e+00 1.00e+00f 1\n", - " 3 9.7843459e+06 0.00e+00 1.14e+02 -1.0 1.49e+00 1.9 1.00e+00 3.82e-01f 1\n", - " 4 9.7800986e+06 0.00e+00 1.22e+02 -1.0 5.15e-01 2.4 1.00e+00 1.00e+00f 1\n", - " 5 9.7555260e+06 0.00e+00 1.61e+02 -1.0 2.17e+00 1.9 1.00e+00 1.00e+00f 1\n", - " 6 9.6448042e+06 0.00e+00 1.91e+02 -1.0 9.04e+00 1.4 1.00e+00 1.00e+00f 1\n", - " 7 9.3871843e+06 0.00e+00 1.90e+02 -1.0 2.21e+01 0.9 1.00e+00 1.00e+00f 1\n", - " 8 8.0296328e+06 0.00e+00 1.38e+03 -1.0 1.41e+03 - 1.00e+00 8.83e-02f 1\n", - " 9 5.9597298e+06 0.00e+00 2.70e+04 -1.0 9.35e+02 - 8.77e-01 1.00e+00f 1\n", + " 0 3.6555128e+07 0.00e+00 9.95e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n", + " 1 3.6554506e+07 0.00e+00 1.01e+02 -1.0 1.32e+00 2.0 1.00e+00 3.55e-02f 1\n", + " 2 3.6549249e+07 0.00e+00 1.03e+02 -1.0 3.85e-01 2.4 1.00e+00 1.00e+00f 1\n", + " 3 3.6539988e+07 0.00e+00 1.14e+02 -1.0 1.49e+00 1.9 1.00e+00 3.81e-01f 1\n", + " 4 3.6530570e+07 0.00e+00 1.22e+02 -1.0 5.16e-01 2.4 1.00e+00 1.00e+00f 1\n", + " 5 3.6475680e+07 0.00e+00 1.62e+02 -1.0 2.18e+00 1.9 1.00e+00 1.00e+00f 1\n", + " 6 3.6226032e+07 0.00e+00 1.93e+02 -1.0 9.16e+00 1.4 1.00e+00 1.00e+00f 1\n", + " 7 3.5640915e+07 0.00e+00 1.94e+02 -1.0 2.24e+01 0.9 1.00e+00 1.00e+00f 1\n", + " 8 3.1876851e+07 0.00e+00 1.85e+03 -1.0 2.73e+03 - 1.00e+00 5.57e-02f 1\n", + " 9 2.1117814e+07 0.00e+00 2.30e+04 -1.0 1.61e+03 - 8.39e-01 5.00e-01f 2\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 10 5.7574033e+06 0.00e+00 2.69e+04 -1.0 1.43e+02 3.2 1.00e+00 8.78e-04f 1\n", - " 11 3.4494750e+06 0.00e+00 2.37e+03 -1.0 4.62e+01 2.7 2.57e-01 1.00e+00f 1\n", - " 12 3.4491800e+06 0.00e+00 2.20e+03 -1.0 1.39e-01 2.2 7.30e-02 1.00e+00f 1\n", - " 13 3.3749119e+06 0.00e+00 1.53e+03 -1.0 7.03e-01 1.7 1.14e-03 1.00e+00f 1\n", - " 14 3.0799543e+06 0.00e+00 2.38e+03 -1.0 8.78e+00 1.3 1.00e+00 1.00e+00f 1\n", - " 15 2.1968496e+06 0.00e+00 1.83e+03 -1.0 1.10e+01 1.7 2.12e-01 1.00e+00f 1\n", - " 16 2.0009588e+06 0.00e+00 2.33e+02 -1.0 2.33e+01 1.2 1.00e+00 3.97e-01f 2\n", - " 17 1.8871185e+06 0.00e+00 6.25e+02 -1.0 1.99e+04 - 1.00e+00 1.51e-03f 2\n", - " 18 3.4603832e+05 0.00e+00 4.63e+02 -1.0 7.69e+02 - 5.71e-01 1.00e+00f 1\n", - " 19 2.9278934e+05 0.00e+00 5.62e+03 -1.0 6.15e+02 - 1.00e+00 3.93e-01f 2\n", + " 10 1.0245145e+07 0.00e+00 3.87e+04 -1.0 3.09e+03 - 6.87e-04 5.00e-01f 2\n", + " 11 6.0210311e+06 0.00e+00 1.00e+04 -1.0 2.64e+02 0.5 1.80e-03 6.25e-02f 5\n", + " 12 5.3483055e+05 0.00e+00 7.97e+03 -1.0 2.24e+03 - 1.00e+00 1.00e+00f 1\n", + " 13 9.2185651e+04 0.00e+00 2.57e+02 -1.0 4.36e+00 -0.0 1.00e+00 5.00e-01f 2\n", + " 14 5.9268198e+04 0.00e+00 1.83e+03 -1.0 4.42e+02 - 1.00e+00 1.00e+00f 1\n", + " 15 4.9517590e+04 0.00e+00 3.38e+02 -1.0 2.57e+02 - 1.00e+00 1.00e+00f 1\n", + " 16 4.7511205e+04 0.00e+00 2.99e+02 -1.0 1.99e+02 - 1.00e+00 1.00e+00f 1\n", + " 17 4.7312872e+04 0.00e+00 2.15e+01 -1.0 6.21e+01 - 1.00e+00 1.00e+00f 1\n", + " 18 4.7309540e+04 0.00e+00 7.92e-01 -1.0 1.09e+01 - 1.00e+00 1.00e+00f 1\n", + " 19 4.7309538e+04 0.00e+00 2.26e-04 -1.7 1.97e-01 - 1.00e+00 1.00e+00f 1\n", "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n", - " 20 3.4113659e+04 0.00e+00 1.13e+03 -1.0 4.42e+02 - 1.00e+00 1.00e+00f 1\n", - " 21 3.1843914e+04 0.00e+00 5.12e+03 -1.0 3.92e+02 - 1.00e+00 1.00e+00f 1\n", - " 22 6.7091573e+03 0.00e+00 2.01e+02 -1.0 1.43e+02 - 1.00e+00 1.00e+00f 1\n", - " 23 6.3069478e+03 0.00e+00 1.98e+02 -1.0 1.07e+02 - 1.00e+00 1.00e+00f 1\n", - " 24 6.2784889e+03 0.00e+00 2.16e-01 -1.0 4.30e+00 - 1.00e+00 1.00e+00f 1\n", - " 25 6.2784887e+03 0.00e+00 9.08e-05 -1.7 7.43e-02 - 1.00e+00 1.00e+00f 1\n", - " 26 6.2784887e+03 0.00e+00 2.89e-08 -3.8 1.41e-03 - 1.00e+00 1.00e+00f 1\n", - " 27 6.2784887e+03 0.00e+00 1.13e-11 -5.7 1.06e-05 - 1.00e+00 1.00e+00f 1\n", - " 28 6.2784887e+03 0.00e+00 1.87e-11 -8.6 1.31e-07 - 1.00e+00 1.00e+00f 1\n", + " 20 4.7309538e+04 0.00e+00 9.99e-09 -3.8 1.13e-03 - 1.00e+00 1.00e+00f 1\n", + " 21 4.7309538e+04 0.00e+00 3.98e-11 -5.7 7.76e-06 - 1.00e+00 1.00e+00f 1\n", + " 22 4.7309538e+04 0.00e+00 8.42e-12 -8.6 9.62e-08 - 1.00e+00 1.00e+00f 1\n", "\n", - "Number of Iterations....: 28\n", + "Number of Iterations....: 22\n", "\n", " (scaled) (unscaled)\n", - "Objective...............: 1.0465892180512249e+02 6.2784887190892987e+03\n", - "Dual infeasibility......: 1.8690096218380457e-11 1.1212188721406439e-09\n", + "Objective...............: 3.5718790719555545e+02 4.7309538308051320e+04\n", + "Dual infeasibility......: 8.4202737638256645e-12 1.1152652600187092e-09\n", "Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n", - "Complementarity.........: 2.5059036395396755e-09 1.5032915933598514e-07\n", - "Overall NLP error.......: 2.5059036395396755e-09 1.5032915933598514e-07\n", + "Complementarity.........: 2.5059035951042295e-09 3.3190693117155519e-07\n", + "Overall NLP error.......: 2.5059035951042295e-09 3.3190693117155519e-07\n", "\n", "\n", - "Number of objective function evaluations = 44\n", - "Number of objective gradient evaluations = 29\n", + "Number of objective function evaluations = 46\n", + "Number of objective gradient evaluations = 23\n", "Number of equality constraint evaluations = 0\n", "Number of inequality constraint evaluations = 0\n", "Number of equality constraint Jacobian evaluations = 0\n", "Number of inequality constraint Jacobian evaluations = 0\n", - "Number of Lagrangian Hessian evaluations = 28\n", - "Total CPU secs in IPOPT (w/o function evaluations) = 0.305\n", - "Total CPU secs in NLP function evaluations = 0.197\n", + "Number of Lagrangian Hessian evaluations = 22\n", + "Total CPU secs in IPOPT (w/o function evaluations) = 0.291\n", + "Total CPU secs in NLP function evaluations = 0.187\n", "\n", "EXIT: Optimal Solution Found.\n", "Generic Execution stats\n", " status: \"first-order stationary\"\n", - " objective value: 6278.488719089299\n", + " objective value: 47309.53830805132\n", " primal feasibility: 0.0\n", - " dual feasibility: 1.1212188721406439e-9\n", - " solution: [-3.5299388841858277 4.6934955973753905 3228.2702299739562]\n", - " iterations: 28\n", - " elapsed time: 0.502\n", + " dual feasibility: 1.1152652600187092e-9\n", + " solution: [-3.560563633278696 4.564087164319889 5769.239983066867]\n", + " iterations: 22\n", + " elapsed time: 0.478\n", " solver specific:\n", - " multipliers_U: [4.2586901315371986e-8 0.0 0.0]\n", + " multipliers_U: [9.321752520786941e-8 0.0 0.0]\n", " multipliers_L: [0.0 0.0 0.0]\n", " multipliers_con: ∅\n", " internal_msg: :Solve_Succeeded\n" @@ -1588,16 +1705,11 @@ } ], "source": [ - "df = data[findall(data[:CountryExp] .== \"Brazil\"),:]\n", + "df = data[findall(data.CountryExp .== \"Brazil\"),:]\n", "y = reverse(df.NewConfCases);\n", "y = cumsum(y)\n", "y = y[findfirst(y .≥ 100):end]\n", "\n", - "#y = y[1:end-1]\n", - "# A partir de quando Δy > 0\n", - "#y = y[findlast(y .== 0)+1:end]\n", - "#y = cumsum(y[findlast(y .== 0)+1:end])\n", - "\n", "n = length(y)\n", "x = range(1, n, length=n)\n", "x = range(0, 1, length=n)\n", @@ -1615,7 +1727,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -1624,189 +1736,205 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", "0.00\n", "\n", - "\n", - "0.25\n", - "\n", - "\n", - "0.50\n", - "\n", - "\n", - "0.75\n", - "\n", - "\n", - "1.00\n", - "\n", - "\n", - "500\n", - "\n", - "\n", - "1000\n", - "\n", - "\n", - "1500\n", - "\n", - "\n", - "2000\n", - "\n", - "\n", - "2500\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "0.25\n", + "\n", + "\n", + "0.50\n", + "\n", + "\n", + "0.75\n", + "\n", + "\n", + "1.00\n", + "\n", + "\n", + "0\n", + "\n", + "\n", + "1000\n", + "\n", + "\n", + "2000\n", + "\n", + "\n", + "3000\n", + "\n", + "\n", + "4000\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n" ] }, - "execution_count": 32, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -1837,7 +1965,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1846,481 +1974,507 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", + " \n", " \n", " \n", "\n", - "\n", "\n", - " \n", - " \n", + " \n", + " \n", " \n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "5\n", - "\n", - "\n", - "10\n", + "\n", + "10\n", "\n", - "\n", - "15\n", - "\n", - "\n", - "20\n", - "\n", - "\n", - "25\n", - "\n", - "\n", - "30\n", - "\n", - "\n", - "35\n", - "\n", - "\n", - "100\n", - "\n", - "\n", - "500\n", - "\n", - "\n", - "1000\n", - "\n", - "\n", - "5000\n", - "\n", - "\n", - "10000\n", - "\n", - 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"\n", - "\n", - "\n", - "Brazil\n", + "\n", + "\n", + "Brazil\n", "\n", - "\n", - "\n", - "Canada\n", + "\n", + "\n", + "Canada\n", "\n", - "\n", - "\n", - "South_Korea\n", + "\n", + "\n", + "South_Korea\n", "\n", - "\n", - "\n", - "United_States_of_America\n", + "\n", + "\n", + "United_States_of_America\n", "\n", - "\n", - "\n", - "Turkey\n", + "\n", + "\n", + "Turkey\n", "\n", - "\n", - "\n", - "Singapore\n", + "\n", + "\n", + "Singapore\n", "\n", - "\n", - "\n", - "Japan\n", + "\n", + "\n", + "Japan\n", "\n", - "\n", - "\n", - "Italy\n", + "\n", + "\n", + "Italy\n", "\n", - "\n", - "Brazil\n", + "\n", + "Brazil\n", "\n", - "\n", - "Canada\n", + "\n", + "Canada\n", "\n", - "\n", - "South_Korea\n", + "\n", + "South_Korea\n", "\n", - "\n", - "United_States_of_America\n", + "\n", + "United_States_of_America\n", "\n", - "\n", - "Turkey\n", + "\n", + "Turkey\n", "\n", - "\n", - "Singapore\n", + "\n", + "Singapore\n", "\n", - "\n", - "Japan\n", + "\n", + "Japan\n", "\n", - "\n", - "Italy\n", + "\n", + "Italy\n", "\n", - "\n", - "Tx 1.1\n", + "\n", + "Tx 1.1\n", "\n", - "\n", - "Tx 1.35\n", + "\n", + "Tx 1.35\n", "\n", - "\n", - "Tx 2.0\n", + "\n", + "Tx 2.0\n", "\n", "\n" ] }, - "execution_count": 131, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2329,10 +2483,11 @@ "using Colors\n", "\n", "plot(size=(1200,800), yaxis=:log)\n", + "#plot(size=(600,400), yaxis=:log)\n", "dmax = ymax = 0.0\n", "countries = [\"Brazil\", \"Canada\", \"South_Korea\", \"United_States_of_America\", \"Turkey\",\n", " \"Singapore\", \"Japan\", \"Italy\"]\n", - "clrs = distinguishable_colors(length(countries) + 1, RGB(0, 0, 0))[2:end]\n", + "clrs = distinguishable_colors(length(countries) + 1, [RGB(0, 0, 0), RGB(1.0, 0.0, 0.0)])[2:end]\n", "for (j,c) in enumerate(countries)\n", " df = data[findall(data[!,:CountryExp] .== c),:]\n", " y = reverse(df.NewConfCases);\n", @@ -2343,7 +2498,7 @@ " lw = c == \"Brazil\" ? 4 : 1\n", " plot!(y, label=\"\", c=clrs[j], lw=lw)\n", " scatter!(y, label=c, m=(3,stroke(0),clrs[j]))\n", - " annotate!([length(y) + 2], [y[end] * 0.9], text(c, 8))\n", + " annotate!([length(y)], [y[end] * 0.9], text(c, 8))\n", " \n", " dmax = max(dmax, length(y))\n", " ymax = max(ymax, y[end])\n", @@ -2379,11 +2534,209 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "png(\"covid-2019-03-26-casos-por-pais\")" + "png(\"covid-2019-03-30-casos-por-pais\")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "91" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "NT = length(unique(data.dateRep))\n", + "y = zeros(length(NT), length(countries))\n", + "for (i,dt) in enumerate(unique(data.dateRep))\n", + " if data\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "91" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "length(unique(data.dateRep))" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8-element Array{Int64,1}:\n", + " 88\n", + " 91\n", + " 91\n", + " 91\n", + " 17\n", + " 91\n", + " 91\n", + " 91" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[data[findall(data.CountryExp .== c),:].NewConfCases for c in countries]" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "75" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "┌ Info: Saved animation to \n", + "│ fn = /home/abel/Documents/streaming/notebooks/covid-new-por-sum.gif\n", + "└ @ Plots /home/abel/.julia/packages/Plots/qZHsp/src/animation.jl:98\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "Plots.AnimatedGif(\"/home/abel/Documents/streaming/notebooks/covid-new-por-sum.gif\")" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gr(size=(1000, 1000))\n", + "\n", + "countries = [\"Brazil\", \"Canada\", \"South_Korea\", \"United_States_of_America\",\n", + " \"Turkey\",\"Singapore\", \"Japan\", \"Italy\"]\n", + "clrs = distinguishable_colors(length(countries) + 1,\n", + " [RGB(0, 0, 0), RGB(1.0, 0.0, 0.0)])[2:end]\n", + "\n", + "dias = unique(data.dateRep)\n", + "NT = length(dias)\n", + "y = zeros(NT, length(countries))\n", + "x = zeros(NT, length(countries))\n", + "for (j,c) in enumerate(countries)\n", + " yc = data[findall(data.CountryExp .== c),:].NewConfCases\n", + " y[1:length(yc),j] .= yc\n", + " y[:,j] = reverse(y[:,j])\n", + " x[:,j] = cumsum(y[:,j])\n", + "end\n", + "\n", + "#I = maximum([findlast(y[:,j] .== 0) + 1 for j = 1:length(countries)]):NT\n", + "I = minimum([findfirst(x[:,j] .≥ 100) for j = 1:length(countries)]):NT\n", + "x = x[I,:]\n", + "y = y[I,:]\n", + "dias = dias[I]\n", + "NT = length(dias)\n", + "\n", + "n = NT\n", + "t = range(1, n, length=10n)\n", + "\n", + "tk = 100\n", + "TKS = [100]\n", + "TKS_L = [\"100\"]\n", + "while tk ≤ maximum(x)\n", + " push!(TKS, 5tk, 10tk)\n", + " push!(TKS_L, string(5tk), string(10tk))\n", + " tk = 10tk\n", + "end\n", + "\n", + "anim = Animation()\n", + "\n", + "for ti in t\n", + " N = floor(Int, ti)\n", + " plot(xaxis=:log, yaxis=:log, leg=false)\n", + " anyplot = false\n", + " for (j,c) in enumerate(countries)\n", + " k = findfirst(x[:,j] .≥ 100)\n", + " \n", + " if N < k\n", + " continue\n", + " end\n", + " anyplot = true\n", + " plot!(x[k:N,j], y[k:N,j], c=clrs[j])\n", + " scatter!(x[k:N,j], y[k:N,j], c=clrs[j])\n", + " λ = ti - N\n", + " if N < n\n", + " plot!([x[N,j], x[N,j] * (1 - λ) + x[N+1,j] * λ],\n", + " [y[N,j], y[N,j] * (1 - λ) + y[N+1,j] * λ], c=clrs[j])\n", + " annotate!([x[N,j] * (1 - λ) + x[N+1,j] * λ],\n", + " [y[N,j] * (1 - λ) + y[N+1,j] * λ] * 1.1, text(c, 8))\n", + " else\n", + " annotate!([x[N,j]], [y[N,j]], text(c, 8))\n", + " end\n", + " #lw = c == \"Brazil\" ? 4 : 1\n", + " end\n", + " if !anyplot\n", + " continue\n", + " end\n", + " xlims!(90, maximum(x) * 2)\n", + " ylims!(1, maximum(y) * 2)\n", + " \n", + " #plot!(legend=:outerright)\n", + " xticks!(TKS, TKS_L)\n", + " xlabel!(\"Número total de casos confirmados\")\n", + " ylabel!(\"Novos casos confirmados\")\n", + " \n", + " frame(anim)\n", + "end\n", + "gif(anim, \"covid-new-por-sum.gif\", fps=20)" ] }, { diff --git "a/avaliacao-cidamo-2020s1/.ipynb_checkpoints/Avalia\303\247\303\243o CiDAMO - 2020s1-checkpoint.ipynb" "b/avaliacao-cidamo-2020s1/.ipynb_checkpoints/Avalia\303\247\303\243o CiDAMO - 2020s1-checkpoint.ipynb" new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ "b/avaliacao-cidamo-2020s1/.ipynb_checkpoints/Avalia\303\247\303\243o CiDAMO - 2020s1-checkpoint.ipynb" @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/avaliacao-cidamo-2020s1/Avalia\303\247\303\243o CiDAMO - 2020s1.ipynb" "b/avaliacao-cidamo-2020s1/Avalia\303\247\303\243o CiDAMO - 2020s1.ipynb" new file mode 100644 index 0000000..825b139 --- /dev/null +++ "b/avaliacao-cidamo-2020s1/Avalia\303\247\303\243o CiDAMO - 2020s1.ipynb" @@ -0,0 +1,29481 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Plots.GRBackend()" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using CSV, DataFrames, Plots\n", + "gr()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

5 rows × 7 columns

Idsalariograu_escolaridadeidadeexperiênciaestado_civilescore
Int64Float64String⍰Int64Float64⍰String⍰Float64
105117.08pós-graduação6228.9922separado350.194
219934.72médio completo6543.1975viúvo347.057
322922.7fund. completo180.0divorciado203.99
431562.23fund. incompleto211.61746divorciado195.43
542460.49médio incompleto212.96212união estável formal235.842
" + ], + "text/latex": [ + "\\begin{tabular}{r|ccccccc}\n", + "\t& Id & salario & grau\\_escolaridade & idade & experiência & estado\\_civil & escore\\\\\n", + "\t\\hline\n", + "\t& Int64 & Float64 & String⍰ & Int64 & Float64⍰ & String⍰ & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 0 & 5117.08 & pós-graduação & 62 & 28.9922 & separado & 350.194 \\\\\n", + "\t2 & 1 & 9934.72 & médio completo & 65 & 43.1975 & viúvo & 347.057 \\\\\n", + "\t3 & 2 & 2922.7 & fund. completo & 18 & 0.0 & divorciado & 203.99 \\\\\n", + "\t4 & 3 & 1562.23 & fund. incompleto & 21 & 1.61746 & divorciado & 195.43 \\\\\n", + "\t5 & 4 & 2460.49 & médio incompleto & 21 & 2.96212 & união estável formal & 235.842 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "5×7 DataFrame. Omitted printing of 2 columns\n", + "│ Row │ Id │ salario │ grau_escolaridade │ idade │ experiência │\n", + "│ │ \u001b[90mInt64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString⍰\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mFloat64⍰\u001b[39m │\n", + "├─────┼───────┼─────────┼───────────────────┼───────┼─────────────┤\n", + "│ 1 │ 0 │ 5117.08 │ pós-graduação │ 62 │ 28.9922 │\n", + "│ 2 │ 1 │ 9934.72 │ médio completo │ 65 │ 43.1975 │\n", + "│ 3 │ 2 │ 2922.7 │ fund. completo │ 18 │ 0.0 │\n", + "│ 4 │ 3 │ 1562.23 │ fund. incompleto │ 21 │ 1.61746 │\n", + "│ 5 │ 4 │ 2460.49 │ médio incompleto │ 21 │ 2.96212 │" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_treino = CSV.read(\"escore_treino.csv\")\n", + "data_teste = CSV.read(\"escore_teste.csv\")\n", + "first(data_treino, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y1\n", + "\n", + "\n" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter(df.experiência, df.escore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Auxiliares" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "r2_score (generic function with 1 method)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using LinearAlgebra, Statistics\n", + "function r2_score(yreal, ypred)\n", + " return 1 - sum( (yreal - ypred).^2 ) / sum( (yreal .- mean(yreal)).^2 )\n", + "end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## O mais simples - regressão linear escore ~ salário" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5972886483769406" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = df.salario\n", + "y = df.escore\n", + "n = length(x)\n", + "X = [ones(n) x]\n", + "β = (X' * X) \\ (X' * y)\n", + "y_pred = X * β\n", + "r2_score(y, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + 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"\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y1\n", + "\n", + "\n", + "\n", + "y2\n", + "\n", + "\n" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = df.salario\n", + "y = df.escore\n", + "n = length(x)\n", + "X = [ones(n) log.(x) log.(x).^2]\n", + "β = (X' * X) \\ (X' * y)\n", + "y_pred = X * β\n", + "\n", + "scatter(df.salario, df.escore, xaxis=:log)\n", + "title!(string(r2_score(y, y_pred)))\n", + "xg = sort(x)\n", + "plot!(xg, β[1] .+ β[2] * log.(xg) + β[3] * log.(xg).^2, c=:red, lw=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outras variáveis numéricas" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6610768584918192" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = df.escore\n", + "n = length(x)\n", + "\n", + "X = [ones(n) df.salario df.idade df.experiência]\n", + "β = (X' * X) \\ (X' * y)\n", + "y_pred = X * β\n", + "r2_score(y, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"escore_sub.csv\"" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dft = copy(data_teste)\n", + "dft[findall(dft.experiência .=== missing), :experiência] .= 0\n", + "Xte = [ones(size(dft, 1)) dft.salario dft.idade dft.experiência]\n", + "y_pred = Xte * β\n", + "dfsub = DataFrame(:Id => dft.Id, :escore => y_pred)\n", + "CSV.write(\"escore_sub.csv\", dfsub)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Variáveis categóricas" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

5 rows × 7 columns

Idsalariograu_escolaridadeidadeexperiênciaestado_civilescore
Int64Float64String⍰Int64Float64⍰String⍰Float64
105117.08pós-graduação6228.9922separado350.194
219934.72médio completo6543.1975viúvo347.057
322922.7fund. completo180.0divorciado203.99
431562.23fund. incompleto211.61746divorciado195.43
542460.49médio incompleto212.96212união estável formal235.842
" + ], + "text/latex": [ + "\\begin{tabular}{r|ccccccc}\n", + "\t& Id & salario & grau\\_escolaridade & idade & experiência & estado\\_civil & escore\\\\\n", + "\t\\hline\n", + "\t& Int64 & Float64 & String⍰ & Int64 & Float64⍰ & String⍰ & Float64\\\\\n", + "\t\\hline\n", + "\t1 & 0 & 5117.08 & pós-graduação & 62 & 28.9922 & separado & 350.194 \\\\\n", + "\t2 & 1 & 9934.72 & médio completo & 65 & 43.1975 & viúvo & 347.057 \\\\\n", + "\t3 & 2 & 2922.7 & fund. completo & 18 & 0.0 & divorciado & 203.99 \\\\\n", + "\t4 & 3 & 1562.23 & fund. incompleto & 21 & 1.61746 & divorciado & 195.43 \\\\\n", + "\t5 & 4 & 2460.49 & médio incompleto & 21 & 2.96212 & união estável formal & 235.842 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "5×7 DataFrame. Omitted printing of 2 columns\n", + "│ Row │ Id │ salario │ grau_escolaridade │ idade │ experiência │\n", + "│ │ \u001b[90mInt64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mString⍰\u001b[39m │ \u001b[90mInt64\u001b[39m │ \u001b[90mFloat64⍰\u001b[39m │\n", + "├─────┼───────┼─────────┼───────────────────┼───────┼─────────────┤\n", + "│ 1 │ 0 │ 5117.08 │ pós-graduação │ 62 │ 28.9922 │\n", + "│ 2 │ 1 │ 9934.72 │ médio completo │ 65 │ 43.1975 │\n", + "│ 3 │ 2 │ 2922.7 │ fund. completo │ 18 │ 0.0 │\n", + "│ 4 │ 3 │ 1562.23 │ fund. incompleto │ 21 │ 1.61746 │\n", + "│ 5 │ 4 │ 2460.49 │ médio incompleto │ 21 │ 2.96212 │" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first(df, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + 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+ "\n" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict_escolaridade = Dict(\n", + " \"fund. incompleto\" => 0,\n", + " \"fund. completo\" => 1,\n", + " \"médio incompleto\" => 2,\n", + " \"médio completo\" => 3,\n", + " \"superior incompleto\" => 4,\n", + " \"superior completo\" => 5,\n", + " \"pós-graduação\" => 6)\n", + "\n", + "df[!,:grau_esc_num] = [dict_escolaridade[x] for x in df.grau_escolaridade]\n", + "scatter(df.escore, df.grau_esc_num)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + 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DF.salario.^2 DF.idade DF.experiência DF.grau_esc_num DF.grau_esc_num.^2 DF.grau_esc_num .* DF.salario]\n", + "\n", + "X = F(df)\n", + "β = (X' * X) \\ (X' * y)\n", + "y_pred = X * β\n", + "r2_score(y, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"escore_sub.csv\"" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dft = copy(data_teste)\n", + "dft[findall(dft.grau_escolaridade .=== missing), :grau_escolaridade] .= \"fund. incompleto\"\n", + "dft[!,:grau_esc_num] = [dict_escolaridade[x] for x in dft.grau_escolaridade]\n", + "dft[findall(dft.experiência .=== missing), :experiência] .= 0\n", + "Xte = F(dft)\n", + "y_pred = Xte * β\n", + "dfsub = DataFrame(:Id => dft.Id, :escore => y_pred)\n", + "CSV.write(\"escore_sub.csv\", dfsub)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.4.0", + "language": "julia", + "name": "julia-1.4" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.4.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/avaliacao-cidamo-2020s1/cidamo-2020s1-p1.zip b/avaliacao-cidamo-2020s1/cidamo-2020s1-p1.zip new file mode 100644 index 0000000..1af8851 Binary files /dev/null and b/avaliacao-cidamo-2020s1/cidamo-2020s1-p1.zip differ diff --git a/avaliacao-cidamo-2020s1/escore_exemplo.csv b/avaliacao-cidamo-2020s1/escore_exemplo.csv new file mode 100644 index 0000000..07418db --- /dev/null +++ b/avaliacao-cidamo-2020s1/escore_exemplo.csv @@ -0,0 +1,1001 @@ +Id,escore +3000,296.9514044724335 +3001,296.9514044724335 +3002,296.9514044724335 +3003,296.9514044724335 +3004,296.9514044724335 +3005,296.9514044724335 +3006,296.9514044724335 +3007,296.9514044724335 +3008,296.9514044724335 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