black

src/BaselineClassifiersBinary.ipynb

@@ -356,7 +356,6 @@
     "    print(lr_pipeline)\n",
     "    print(classification_report(y_test, predict_lr, digits=4))\n",
     "\n",
-    "    \n",
     "    return (\n",
     "        (\n",
     "            {\n",
@@ -396,7 +395,6 @@
     "    plt.show()\n",
     "\n",
     "\n",
-    "\n",
     "data2, nb_mideind, svc_mideind, lr_mideind = classify(ICELANDIC_MIDEIND_CSV)\n",
     "data3, nb_google, svc_google, lr_google = classify(ICELANDIC_GOOGLE_CSV)\n",
     "data1, nb_english, svc_english, lr_english = classify(ENGLISH_CSV)\n",

src/generate_classification_report.py

@@ -110,11 +110,13 @@ def generate_report(self, accuracy):
                 prediction = torch.max(outputs.logits, dim=1)
                 y_true.extend(labels.tolist())
                 y_pred.extend(prediction.indices.tolist())
-        
+
         if accuracy:
             acc = accuracy_score(y_true, y_pred)
             return acc
-        return classification_report(y_true, y_pred, output_dict=True) # NOTE: can use this if you want to print classification report
+        return classification_report(
+            y_true, y_pred, output_dict=True
+        )  # NOTE: can use this if you want to print classification report
 
 
 class DataFrameLoader:
@@ -165,7 +167,6 @@ def generate_report(filename, folder, device):
     print("Loading model from folder {} using file {}".format(folder, filename))
     dfl = DataFrameLoader(filename)
     return call_model(dfl.X_all, dfl.y_all, folder, device)
-
 
 
 def eval_files():

src/generate_report.ipynb

@@ -64,38 +64,48 @@
     "import random\n",
     "import generate_classification_report as gcr\n",
     "\n",
-    "PATH1 = ''\n",
-    "PATH2 = ''\n",
+    "PATH1 = \"\"\n",
+    "PATH2 = \"\"\n",
     "\n",
     "d1 = pd.read_csv(PATH1)\n",
     "d2 = pd.read_csv(PATH2)\n",
-    "d1.drop(['num', 'rating', 'id'], axis=1, inplace=True)\n",
-    "d2.drop(['movie', 'rating'], axis=1, inplace=True)\n",
+    "d1.drop([\"num\", \"rating\", \"id\"], axis=1, inplace=True)\n",
+    "d2.drop([\"movie\", \"rating\"], axis=1, inplace=True)\n",
     "\n",
     "\n",
-    "df_orig = pd.merge(d1, d2, how='outer')\n",
+    "df_orig = pd.merge(d1, d2, how=\"outer\")\n",
     "\n",
-    "device = 'cuda'\n",
-    "model = './electra-base-google-batch8-remove-noise-model/'\n",
+    "device = \"cuda\"\n",
+    "model = \"./electra-base-google-batch8-remove-noise-model/\"\n",
     "\n",
     "\n",
     "total = 0\n",
     "for i in range(0, 10):\n",
     "    r = random.randint(0, 10000)\n",
-    "    \n",
-    "    fifty_negative = df_orig.where(lambda x: x['sentiment'] == 'Negative').dropna().sample(n=50, random_state=r)\n",
-    "    fifty_positive = df_orig.where(lambda x: x['sentiment'] == 'Positive').dropna().sample(n=50, random_state=r)\n",
     "\n",
-    "    new_df = pd.merge(fifty_negative, fifty_positive, on=['sentiment', 'review'], how='outer')\n",
-    "    new_df.sentiment = new_df.sentiment.apply(lambda x: 1 if x == 'Positive' else 0)\n",
+    "    fifty_negative = (\n",
+    "        df_orig.where(lambda x: x[\"sentiment\"] == \"Negative\")\n",
+    "        .dropna()\n",
+    "        .sample(n=50, random_state=r)\n",
+    "    )\n",
+    "    fifty_positive = (\n",
+    "        df_orig.where(lambda x: x[\"sentiment\"] == \"Positive\")\n",
+    "        .dropna()\n",
+    "        .sample(n=50, random_state=r)\n",
+    "    )\n",
+    "\n",
+    "    new_df = pd.merge(\n",
+    "        fifty_negative, fifty_positive, on=[\"sentiment\", \"review\"], how=\"outer\"\n",
+    "    )\n",
+    "    new_df.sentiment = new_df.sentiment.apply(lambda x: 1 if x == \"Positive\" else 0)\n",
     "    X_all = new_df.review\n",
     "    y_all = new_df.sentiment\n",
     "    accuracy = gcr.call_model(X_all, y_all, model, device, accuracy=True)\n",
     "    total += accuracy\n",
-    "    print('acc: {0:.4f}, seed: {1}, i: {2}'.format(accuracy, r, i))\n",
+    "    print(\"acc: {0:.4f}, seed: {1}, i: {2}\".format(accuracy, r, i))\n",
+    "\n",
     "\n",
-    "    \n",
-    "print('Average accuracy: ', total/10)\n"
+    "print(\"Average accuracy: \", total / 10)"
    ]
   },
   {