finally after temporary platform change

Feed-Forward.ipynb

@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "308eb052",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import gensim.downloader as downloader\n",
+    "import pandas as pd\n",
+    "import csv\n",
+    "from nltk.tokenize import word_tokenize as tokenize\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "212c0f2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NeuralNetworkModel(torch.nn.Module):\n",
+    "    \n",
+    "    def __init__(self, input_size, hidden_size, num_classes):\n",
+    "        super(NeuralNetworkModel, self).__init__()\n",
+    "        self.fc1 = torch.nn.Linear(input_size,hidden_size)\n",
+    "        self.fc2 = torch.nn.Linear(hidden_size,num_classes)\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": 4,
+   "id": "afd13d8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "w2v = downloader.load('word2vec-google-news-300')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f68b0a7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#model + settings\n",
+    "\n",
+    "nn = NeuralNetworkModel(300,300,1)\n",
+    "crit = torch.nn.BCELoss()\n",
+    "opti = torch.optim.SGD(nn.parameters(), lr=0.08)\n",
+    "BATCH_SIZE = 5\n",
+    "epochs = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f0713eab",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#trening\n",
+    "\n",
+    "#wczytanie danych\n",
+    "train_data_in = pd.read_csv('train/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t', nrows=3000)\n",
+    "train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t', nrows=3000)\n",
+    "\n",
+    "#preprocessing\n",
+    "train_in = train_data_in[0].str.lower()\n",
+    "train_in = [tokenize(line) for line in train_in]\n",
+    "train_in = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in train_in]\n",
+    "train_ex = train_data_ex[0]\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    nn.train()\n",
+    "    for i in range(0,train_data_ex.shape[0],BATCH_SIZE):\n",
+    "        x = train_in[i:i + BATCH_SIZE]\n",
+    "        x = torch.tensor(x)\n",
+    "        y = train_ex[i:i + BATCH_SIZE]\n",
+    "        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)\n",
+    "        \n",
+    "        opti.zero_grad()\n",
+    "        y_pred = nn(x.float())\n",
+    "        loss = crit(y_pred,y)\n",
+    "        loss.backward()\n",
+    "        opti.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1ab1dce0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-27-b22b834acfd4>:21: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
+      "  np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')\n",
+      "<ipython-input-27-b22b834acfd4>:21: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
+      "  np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')\n"
+     ]
+    }
+   ],
+   "source": [
+    "#dev-0 predict\n",
+    "\n",
+    "#wczytanie danych\n",
+    "dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
+    "dev0_data = dev0_data[0].str.lower()\n",
+    "dev0_data = [tokenize(line) for line in dev0_data]\n",
+    "dev0_data = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in dev0_data]\n",
+    "\n",
+    "dev0_y=[]\n",
+    "nn.eval()\n",
+    "with torch.no_grad():\n",
+    "    for i in range(0, len(dev0_data), BATCH_SIZE):\n",
+    "        x = dev0_data[i:i + BATCH_SIZE]\n",
+    "        x = torch.tensor(x)\n",
+    "        dev0_y_pred = nn(x.float())\n",
+    "\n",
+    "        dev0_y_prediction = (dev0_y_pred > 0.5)\n",
+    "        dev0_y.extend(dev0_y_prediction)\n",
+    "        \n",
+    "#zapis wyników\n",
+    "np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "22941828",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-28-ed4376367760>:21: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
+      "  np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')\n",
+      "<ipython-input-28-ed4376367760>:21: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
+      "  np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')\n"
+     ]
+    }
+   ],
+   "source": [
+    "#test-A predict\n",
+    "\n",
+    "#wczytanie danych\n",
+    "testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
+    "testA_data = testA_data[0].str.lower()\n",
+    "testA_data = [tokenize(line) for line in testA_data]\n",
+    "testA_data = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in testA_data]\n",
+    "\n",
+    "testA_y=[]\n",
+    "nn.eval()\n",
+    "with torch.no_grad():\n",
+    "    for i in range(0, len(testA_data), BATCH_SIZE):\n",
+    "        x = testA_data[i:i + BATCH_SIZE]\n",
+    "        x = torch.tensor(x)\n",
+    "        testA_y_pred = nn(x.float())\n",
+    "\n",
+    "        testA_y_prediction = (testA_y_pred > 0.5)\n",
+    "        testA_y.extend(testA_y_prediction)\n",
+    "        \n",
+    "#zapis wyników\n",
+    "np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\\n')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Feed-Forward.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import torch
+import gensim.downloader as downloader
+import pandas as pd
+import csv
+from nltk.tokenize import word_tokenize as tokenize
+import numpy as np
+
+
+# In[7]:
+
+
+class NeuralNetworkModel(torch.nn.Module):
+    
+    def __init__(self, input_size, hidden_size, num_classes):
+        super(NeuralNetworkModel, self).__init__()
+        self.fc1 = torch.nn.Linear(input_size,hidden_size)
+        self.fc2 = torch.nn.Linear(hidden_size,num_classes)
+        
+    def forward(self,x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+# In[4]:
+
+
+w2v = downloader.load('word2vec-google-news-300')
+
+
+# In[9]:
+
+
+#model + settings
+
+nn = NeuralNetworkModel(300,300,1)
+crit = torch.nn.BCELoss()
+opti = torch.optim.SGD(nn.parameters(), lr=0.08)
+BATCH_SIZE = 5
+epochs = 5
+
+
+# In[12]:
+
+
+#trening
+
+#wczytanie danych
+train_data_in = pd.read_csv('train/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t', nrows=3000)
+train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t', nrows=3000)
+
+#preprocessing
+train_in = train_data_in[0].str.lower()
+train_in = [tokenize(line) for line in train_in]
+train_in = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in train_in]
+train_ex = train_data_ex[0]
+
+for epoch in range(epochs):
+    nn.train()
+    for i in range(0,train_data_ex.shape[0],BATCH_SIZE):
+        x = train_in[i:i + BATCH_SIZE]
+        x = torch.tensor(x)
+        y = train_ex[i:i + BATCH_SIZE]
+        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)
+        
+        opti.zero_grad()
+        y_pred = nn(x.float())
+        loss = crit(y_pred,y)
+        loss.backward()
+        opti.step()
+
+
+# In[27]:
+
+
+#dev-0 predict
+
+#wczytanie danych
+dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\t')
+dev0_data = dev0_data[0].str.lower()
+dev0_data = [tokenize(line) for line in dev0_data]
+dev0_data = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in dev0_data]
+
+dev0_y=[]
+nn.eval()
+with torch.no_grad():
+    for i in range(0, len(dev0_data), BATCH_SIZE):
+        x = dev0_data[i:i + BATCH_SIZE]
+        x = torch.tensor(x)
+        dev0_y_pred = nn(x.float())
+
+        dev0_y_prediction = (dev0_y_pred > 0.5)
+        dev0_y.extend(dev0_y_prediction)
+        
+#zapis wyników
+np.asarray(dev0_y, dtype=np.int32).tofile('dev-0/out.tsv', sep='\n')
+
+
+# In[28]:
+
+
+#test-A predict
+
+#wczytanie danych
+testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\t')
+testA_data = testA_data[0].str.lower()
+testA_data = [tokenize(line) for line in testA_data]
+testA_data = [np.mean([w2v[x] for x in data if x in w2v] or [np.zeros(300)], axis=0) for data in testA_data]
+
+testA_y=[]
+nn.eval()
+with torch.no_grad():
+    for i in range(0, len(testA_data), BATCH_SIZE):
+        x = testA_data[i:i + BATCH_SIZE]
+        x = torch.tensor(x)
+        testA_y_pred = nn(x.float())
+
+        testA_y_prediction = (testA_y_pred > 0.5)
+        testA_y.extend(testA_y_prediction)
+        
+#zapis wyników
+np.asarray(testA_y, dtype=np.int32).tofile('test-A/out.tsv', sep='\n')
+