my brilliant

foo.py

@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[3]:
+
+
+import pathlib
+from collections import Counter
+from sklearn.metrics import *
+import pandas as pd
+
+
+# In[1]:
+
+
+import numpy as np, pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.datasets import fetch_20newsgroups
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import confusion_matrix, accuracy_score
+sns.set() # use seaborn plotting style
+
+
+# In[5]:
+
+
+train_x = pd.read_csv('train/in.tsv', header=None, sep='\t')
+train_y = pd.read_csv('train/expected.tsv', header=None, sep='\t')
+dev_x = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
+dev_y = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
+test_x = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
+
+
+# In[61]:
+
+
+print(dev_y.shape)
+print(dev_x.shape)
+
+
+# In[11]:
+
+
+print(train_x[:15])
+
+
+# In[27]:
+
+
+print(train_x.shape)
+
+
+# In[49]:
+
+
+print(train_y.shape)
+
+
+# In[8]:
+
+
+print(train_y[:15])
+
+
+# In[53]:
+
+
+print(dev_x[:4])
+
+
+# In[119]:
+
+
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfTransformer
+
+vec = CountVectorizer(stop_words='english')
+x1 = vec.fit_transform(train_x[:20000][0])
+tfidf_transformer = TfidfTransformer()
+
+x1_tf = tfidf_transformer.fit_transform(x1)
+
+
+# In[120]:
+
+
+# Build the model
+#model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+clf = MultinomialNB().fit(x1_tf, train_y[:20000][0])
+
+
+# In[121]:
+
+
+# Train the model using the training data
+#model.fit(x1[:][0], train_y[:289541][0])
+# Predict the categories of the test data
+X_new_counts = vec.transform(dev_x[:][0])
+# We call transform instead of fit_transform because it's already been fit
+X_new_tfidf = tfidf_transformer.transform(X_new_counts)
+#predicted_categories = model.predict(dev_x[:][0])
+
+
+# In[122]:
+
+
+predicted = clf.predict(X_new_tfidf)
+
+
+# In[125]:
+
+
+print(predicted[:10])
+
+
+# In[126]:
+
+
+print(predicted.shape)
+
+
+# In[123]:
+
+
+#mat = confusion_matrix(dev_y[:][0],predicted_categories)
+
+print("The accuracy is {}".format(accuracy_score( dev_y[:][0],predicted_categories)))
+ 
+
+
+# In[124]:
+
+
+print('We got an accuracy of',np.mean(predicted == dev_y[:][0])*100, '% over the test data.')
+
+
+# In[130]:
+
+
+np.savetxt("out.tsv",predicted, delimiter="\t", fmt='%d')
+
+
+# In[131]:
+
+
+X_test = vec.transform(test_x[:][0])
+# We call transform instead of fit_transform because it's already been fit
+X_tfidf_test = tfidf_transformer.transform(X_test)
+predicted_test = clf.predict(X_tfidf_test)
+np.savetxt("out.tsv",predicted_test, delimiter="\t", fmt='%d')
+
+
+# In[ ]:
+
+
+
+

pytorch.ipynb

@@ -0,0 +1,548 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lzma\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "from gensim import downloader\n",
+    "from gensim.models import Word2Vec\n",
+    "import gensim.downloader\n",
+    "import pandas as pd\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
+    "\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\10118794\\AppData\\Local\\Temp\\ipykernel_32100\\3675615398.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
+      "\n",
+      "\n",
+      "  train_x = pd.read_csv('train/in.tsv', header=None, sep='\\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)\n",
+      "C:\\Users\\10118794\\AppData\\Local\\Temp\\ipykernel_32100\\3675615398.py:2: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
+      "\n",
+      "\n",
+      "  train_y = pd.read_csv('train/expected.tsv', header=None, sep='\\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)\n",
+      "C:\\Users\\10118794\\AppData\\Local\\Temp\\ipykernel_32100\\3675615398.py:3: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
+      "\n",
+      "\n",
+      "  dev_x = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)\n",
+      "C:\\Users\\10118794\\AppData\\Local\\Temp\\ipykernel_32100\\3675615398.py:4: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
+      "\n",
+      "\n",
+      "  dev_y = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)\n",
+      "C:\\Users\\10118794\\AppData\\Local\\Temp\\ipykernel_32100\\3675615398.py:5: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
+      "\n",
+      "\n",
+      "  test_x = pd.read_csv('test-A/in.tsv', header=None, sep='\\t',quoting=csv.QUOTE_NONE, error_bad_lines=False)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_x = pd.read_csv('train/in.tsv', header=None, sep='\\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)\n",
+    "train_y = pd.read_csv('train/expected.tsv', header=None, sep='\\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)\n",
+    "dev_x = pd.read_csv('dev-0/in.tsv', header=None, sep='\\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)\n",
+    "dev_y = pd.read_csv('dev-0/expected.tsv', header=None, sep='\\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)\n",
+    "test_x = pd.read_csv('test-A/in.tsv', header=None, sep='\\t',quoting=csv.QUOTE_NONE, error_bad_lines=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_x = train_x[0]\n",
+    "dev_x = dev_x[0]\n",
+    "test_x = test_x[0]\n",
+    "train_y = train_y[0]\n",
+    "dev_y = dev_y[0]\n",
+    "train_y = train_y.to_numpy()\n",
+    "dev_y = dev_y.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[==================================================] 100.0% 387.1/387.1MB downloaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "word2vec_100 = downloader.load(\"glove-twitter-100\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_x_w2v = [np.mean([word2vec_100[word.lower()] for word in doc.split() if word.lower() in word2vec_100]\n",
+    "                   or [np.zeros(100, dtype=float)], axis=0) for doc in train_x]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev_x_w2v2 = [np.mean([word2vec_100[word.lower()] for word in doc.split() if word.lower() in word2vec_100]\n",
+    "                   or [np.zeros(100, dtype=float)], axis=0) for doc in dev_x]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_x_w2v = [np.mean([word2vec_100[word.lower()] for word in doc.split() if word.lower() in word2vec_100]\n",
+    "                   or [np.zeros(100, dtype=float)], axis=0) for doc in test_x]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(x_train_w2v))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NeuralNetworkModelx(torch.nn.Module):\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        super(NeuralNetworkModelx, self).__init__()\n",
+    "        self.fc1 = torch.nn.Linear(100,500)\n",
+    "        self.fc2 = torch.nn.Linear(500,1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.fc1(x)\n",
+    "        x = torch.relu(x)\n",
+    "        x = self.fc2(x)\n",
+    "        x = torch.sigmoid(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(model, data):\n",
+    "    model.eval()\n",
+    "    predictions = []\n",
+    "    for x in data:\n",
+    "        X = torch.tensor(np.array(x).astype(np.float32))\n",
+    "        Y_predictions = model(X)\n",
+    "        if Y_predictions[0] > 0.5:\n",
+    "            predictions.append(\"1\")\n",
+    "        else:\n",
+    "            predictions.append(\"0\")\n",
+    "    return predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BATCH_SIZE = 22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FEATURES = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = NeuralNetworkModelx()\n",
+    "criterion = torch.nn.BCELoss()\n",
+    "optimizer = torch.optim.ASGD(model.parameters(), lr=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_loss_acc(model, X_dataset, Y_dataset):\n",
+    "    loss_score = 0\n",
+    "    acc_score = 0\n",
+    "    items_total = 0\n",
+    "    model.eval()\n",
+    "    for i in range(0, Y_dataset.shape[0], BATCH_SIZE):\n",
+    "        X = np.array(X_dataset[i:i+BATCH_SIZE]).astype(np.float32)\n",
+    "        X = torch.tensor(X)\n",
+    "        Y = Y_dataset[i:i+BATCH_SIZE]\n",
+    "        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)\n",
+    "        Y_predictions = model(X)\n",
+    "        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()\n",
+    "        items_total += Y.shape[0]\n",
+    "\n",
+    "        loss = criterion(Y_predictions, Y)\n",
+    "\n",
+    "        loss_score += loss.item() * Y.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5251316127311283 0.7293691876828085\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5236849654193508 0.7303671882284282\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5224315920787511 0.7310509394672956\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5211956211888409 0.7323010301161341\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5201234243425219 0.7329606083314052\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5192769648569354 0.7337203319301469\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5182789765264713 0.7341761660893918\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5173362161154499 0.7348944502191112\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5163200458762819 0.7358717310302197\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5155178654158614 0.7361583540242904\n"
+     ]
+    }
+   ],
+   "source": [
+    "for epoch in range(10):\n",
+    "    loss_score = 0\n",
+    "    acc_score = 0\n",
+    "    items_total = 0\n",
+    "    for i in range(0, train_y.shape[0], BATCH_SIZE):\n",
+    "        x = train_x_w2v[i:i+BATCH_SIZE]\n",
+    "        x = torch.tensor(np.array(x).astype(np.float32))\n",
+    "        y = train_y[i:i+BATCH_SIZE]\n",
+    "        y = torch.tensor(y.astype(np.float32)).reshape(-1, 1)\n",
+    "        y_pred = model(x)\n",
+    "        acc_score += torch.sum((y_pred > 0.5) == y).item()\n",
+    "        items_total += y.shape[0]\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        loss = criterion(y_pred, y)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        loss_score += loss.item() * y.shape[0]\n",
+    "    display(epoch)\n",
+    "    #display(get_loss_acc(model, train_x_w2v, train_y))\n",
+    "    #display(get_loss_acc(model, dev_x_w2v2, dev_y))\n",
+    "    print((loss_score / items_total), (acc_score / items_total))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_dev = predict(model, dev_x_w2v2)\n",
+    "pred_test = predict(model, test_x_w2v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev_pred = [int(i) for i in pred_dev]\n",
+    "test_pred = [int(i) for i in pred_test]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dev_pred = np.array(dev_pred)\n",
+    "test_pred = np.array(test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(dev_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 0, ..., 0, 1, 0])"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dev_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"dev-0/out.tsv\",dev_pred, delimiter=\"\\t\", fmt='%d')\n",
+    "np.savetxt(\"test-A/out.tsv\",test_pred, delimiter=\"\\t\", fmt='%d')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py",
+   "language": "python",
+   "name": "py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

run_pytorch.py

@@ -0,0 +1,305 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[3]:
+
+
+from sklearn.model_selection import train_test_split
+
+from sklearn.datasets import fetch_20newsgroups
+# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics import accuracy_score
+
+
+# In[71]:
+
+
+import numpy as np
+import gensim
+import torch
+import pandas as pd
+from gensim.test.utils import common_texts
+from gensim.models import Word2Vec
+import csv
+
+
+# In[84]:
+
+
+train_x = pd.read_csv('train/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
+train_y = pd.read_csv('train/expected.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
+dev_x = pd.read_csv('dev-0/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
+dev_y = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
+test_x = pd.read_csv('test-A/in.tsv', header=None, sep='\t',quoting=csv.QUOTE_NONE, error_bad_lines=False)
+
+
+# In[85]:
+
+
+print(len(train_x))
+
+
+# In[86]:
+
+
+print(len(train_y))
+
+
+# In[87]:
+
+
+train_y = train_y[0]
+
+
+# In[100]:
+
+
+dev_y = dev_y[0]
+
+
+# In[88]:
+
+
+print(type(train_y))
+
+
+# In[89]:
+
+
+train_y = train_y.to_numpy()
+
+
+# In[102]:
+
+
+dev_y = dev_y.to_numpy()
+
+
+# In[90]:
+
+
+train_x.head
+
+
+# In[91]:
+
+
+dev_x.head()
+
+
+# In[92]:
+
+
+train_x = train_x[0]
+
+
+# In[93]:
+
+
+vec_model = Word2Vec(train_x, vector_size=100, window=5, min_count=1, workers=4)
+
+
+# In[ ]:
+
+
+def w2v(model, data):
+    return np.array([np.mean([model.wv[word] if word in model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in data])
+    
+
+
+# In[ ]:
+
+
+w2v()
+
+
+# In[96]:
+
+
+dev_x = dev_x[0]
+test_x = test_x[0]
+
+
+# In[95]:
+
+
+vec_x_train = np.array([np.mean([vec_model.wv[word] if word in vec_model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in train_x])
+
+
+# In[97]:
+
+
+vec_x_dev = np.array([np.mean([vec_model.wv[word] if word in vec_model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in dev_x])
+vec_x_test = np.array([np.mean([vec_model.wv[word] if word in vec_model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in test_x])
+
+
+# In[36]:
+
+
+X_dev0_w2v = vectorize(vec_model,dev_x)
+X_test_w2v = vectorize(vec_model,test_x)
+
+
+# In[7]:
+
+
+class NeuralNetworkModel(torch.nn.Module):
+
+    def __init__(self):
+        super(NeuralNetworkModel, self).__init__()
+        self.fc1 = torch.nn.Linear(FEAUTERES,500)
+        self.fc2 = torch.nn.Linear(500,1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+# In[37]:
+
+
+criterion = torch.nn.BCELoss()
+optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
+
+
+# In[8]:
+
+
+def get_loss_acc(model, X_dataset, Y_dataset):
+    loss_score = 0
+    acc_score = 0
+    items_total = 0
+    model.eval()
+    for i in range(0, Y_dataset.shape[0], BATCH_SIZE):
+        X = np.array(X_dataset[i:i+BATCH_SIZE]).astype(np.float32)
+        X = torch.tensor(X)
+        Y = Y_dataset[i:i+BATCH_SIZE]
+        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
+        Y_predictions = model(X)
+        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
+        items_total += Y.shape[0]
+
+        loss = criterion(Y_predictions, Y)
+
+        loss_score += loss.item() * Y.shape[0]
+    return (loss_score / items_total), (acc_score / items_total)
+
+
+# In[9]:
+
+
+def predict(model, data):
+    model.eval()
+    predictions = []
+    for x in data:
+        X = torch.tensor(np.array(x).astype(np.float32))
+        Y_predictions = model(X)
+        if Y_predictions[0] > 0.5:
+            predictions.append("1")
+        else:
+            predictions.append("0")
+    return predictions
+
+
+# In[18]:
+
+
+FEAUTERES = 100
+
+
+# In[62]:
+
+
+BATCH_SIZE = 5
+
+
+# In[58]:
+
+
+nn_model = NeuralNetworkModel()
+
+
+# In[103]:
+
+
+for epoch in range(7):
+    loss_score = 0
+    acc_score = 0
+    items_total = 0
+    nn_model.train()
+    for i in range(0, train_y.shape[0], BATCH_SIZE):
+        X = vec_x_train[i:i+BATCH_SIZE]
+        X = torch.tensor(X)
+        Y = train_y[i:i+BATCH_SIZE]
+        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
+        Y_predictions = nn_model(X)
+        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
+        items_total += Y.shape[0]
+
+        optimizer.zero_grad()
+        loss = criterion(Y_predictions, Y)
+        loss.backward()
+        optimizer.step()
+
+        loss_score += loss.item() * Y.shape[0]
+
+    display(epoch)
+    display(get_loss_acc(nn_model,vec_x_train, train_y))
+    display(get_loss_acc(nn_model, vec_x_dev, dev_y))
+
+
+# In[104]:
+
+
+dev_pred = predict(nn_model, vec_x_dev)
+test_pred = predict(nn_model, vec_x_test)
+
+
+# In[105]:
+
+
+dev_pred
+
+
+# In[119]:
+
+
+dev_pred = [int(i) for i in dev_pred]
+test_pred = [int(i) for i in test_pred]
+
+
+# In[120]:
+
+
+dev_pred = np.array(dev_pred)
+test_pred = np.array(test_pred)
+
+
+# In[117]:
+
+
+dev_pred
+
+
+# In[121]:
+
+
+np.savetxt("dev-0/out.tsv",dev_pred, delimiter="\t", fmt='%d')
+
+
+# In[122]:
+
+
+np.savetxt("test-A/out.tsv",test_pred, delimiter="\t", fmt='%d')
+
+
+# In[ ]:
+
+
+
+