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learningmatter-mit · Aug 20, 2021 · bed31b9 · bed31b9
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commit bed31b9
Showing 1 changed file with 58 additions and 21 deletions.
diff --git a/Tutorial.ipynb b/Tutorial.ipynb
@@ -171,14 +171,14 @@
    "outputs": [],
    "source": [
     "NX_GRAPHS = networkx_feat(\n",
-    "    TXT_DATA_PATH, \n",
-    "    MON_SMILES, \n",
-    "    BOND_SMILES, \n",
-    "    FEAT, \n",
-    "    FP_RADIUS_MON, \n",
-    "    FP_RADIUS_BOND, \n",
-    "    FP_BITS_MON, \n",
-    "    FP_BITS_BOND\n",
+    "    TXT_DATA_PATH = TXT_DATA_PATH, \n",
+    "    MON_SMILES = MON_SMILES, \n",
+    "    BOND_SMILES = BOND_SMILES, \n",
+    "    FEAT = FEAT, \n",
+    "    FP_RADIUS_MON = FP_RADIUS_MON, \n",
+    "    FP_RADIUS_BOND = FP_RADIUS_BOND, \n",
+    "    FP_BITS_MON = FP_BITS_MON, \n",
+    "    FP_BITS_BOND = FP_BITS_BOND\n",
     ")"
    ]
   },
@@ -282,7 +282,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sim_matrix = unsup.similarity_matrix(NX_GRAPHS)"
+    "sim_matrix = unsup.similarity_matrix(dict_graphs = NX_GRAPHS)"
    ]
   },
   {
@@ -292,7 +292,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings = unsup.dimensionality_reduction(sim_matrix, method = 'umap', n_neighbors=3)"
+    "embeddings = unsup.dimensionality_reduction(\n",
+    "    matrix = sim_matrix, \n",
+    "    method = 'umap', \n",
+    "    n_neighbors=3)"
    ]
   },
   {
@@ -315,7 +318,11 @@
     }
    ],
    "source": [
-    "unsup.plot_embeddings(embeddings, NX_GRAPHS, DF_PATH, 'UMAP')"
+    "unsup.plot_embeddings(\n",
+    "    embeddings = embeddings, \n",
+    "    NX_GRAPHS = NX_GRAPHS, \n",
+    "    DF_PATH = DF_PATH, \n",
+    "    method = 'UMAP')"
    ]
   },
   {
@@ -333,7 +340,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dgl_dict = MacroDataset(DF_PATH, SEED, TASK, LABELNAME, MODEL, NX_GRAPHS, NORM)"
+    "dgl_dict = MacroDataset(\n",
+    "    DF_PATH = DF_PATH, \n",
+    "    SEED = SEED, \n",
+    "    TASK = TASK, \n",
+    "    LABELNAME = LABELNAME, \n",
+    "    MODEL = MODEL, \n",
+    "    NX_GRAPHS = NX_GRAPHS, \n",
+    "    NORM = NORM)"
    ]
   },
   {
@@ -361,12 +375,24 @@
    ],
    "source": [
     "random.seed(SEED)\n",
+    "\n",
     "macro_supervised = MacroSupervised(\n",
-    "    dgl_dict, \n",
-    "    MON_SMILES, BOND_SMILES, \n",
-    "    FEAT, FP_BITS_MON, FP_BITS_BOND, \n",
-    "    SEED, MODEL, SPLIT, \n",
-    "    NUM_EPOCHS, NUM_WORKERS, CUSTOM_PARAMS, MODEL_PATH, SAVE_MODEL, SAVE_OPT, SAVE_CONFIG)"
+    "    MacroDataset = dgl_dict, \n",
+    "    MON_SMILES = MON_SMILES, \n",
+    "    BOND_SMILES = BOND_SMILES, \n",
+    "    FEAT = FEAT, \n",
+    "    FP_BITS_MON = FP_BITS_MON, \n",
+    "    FP_BITS_BOND = FP_BITS_BOND, \n",
+    "    SEED = SEED, \n",
+    "    MODEL = MODEL, \n",
+    "    SPLIT = SPLIT, \n",
+    "    NUM_EPOCHS = NUM_EPOCHS, \n",
+    "    NUM_WORKERS = NUM_WORKERS, \n",
+    "    CUSTOM_PARAMS = CUSTOM_PARAMS, \n",
+    "    MODEL_PATH = MODEL_PATH, \n",
+    "    SAVE_MODEL = SAVE_MODEL, \n",
+    "    SAVE_OPT = SAVE_OPT, \n",
+    "    SAVE_CONFIG = SAVE_CONFIG)"
    ]
   },
   {
@@ -481,8 +507,13 @@
     "random.seed(SEED)\n",
     "input_graph = dgl_dict.graphs[0]\n",
     "\n",
-    "attribution = Attribution(model_name=MODEL, attribution_type='integrated_gradients')\n",
-    "attribution.calculate_attribution(macro_supervised.model, input_graph)"
+    "attribution = Attribution(\n",
+    "    model_name = MODEL, \n",
+    "    attribution_type = 'integrated_gradients')\n",
+    "\n",
+    "attribution.calculate_attribution(\n",
+    "    model_instance = macro_supervised.model, \n",
+    "    input_graph = input_graph)"
    ]
   },
   {
@@ -511,7 +542,9 @@
     }
    ],
    "source": [
-    "attribution.plot_graph(input_graph, NX_GRAPHS)"
+    "attribution.plot_graph(\n",
+    "    input_graph = input_graph, \n",
+    "    NX_GRAPHS = NX_GRAPHS)"
    ]
   },
   {
@@ -565,7 +598,11 @@
    ],
    "source": [
     "index = 16\n",
-    "mol, bi = attribution.display_substructure('Xyl', MON_SMILES, FP_RADIUS_MON, FP_BITS_MON)\n",
+    "mol, bi = attribution.display_substructure(\n",
+    "    monomer = 'Xyl', \n",
+    "    MON_SMILES_PATH = MON_SMILES, \n",
+    "    RADIUS = FP_RADIUS_MON, \n",
+    "    N_BITS = FP_BITS_MON)\n",
     "Draw.DrawMorganBit(mol, index, bi)"
    ]
   }