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korne 2022-06-14 23:36:56 +02:00
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262
.ipynb_checkpoints/run-checkpoint.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "74100403-147c-42cd-8285-e30778c0fb66",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import gensim\n",
"import torch\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bf211ece-e27a-4119-a1b9-9a9a610cfb46",
"metadata": {},
"outputs": [],
"source": [
"def predict_year(x, path_out, model):\n",
" results = model.predict(x)\n",
" with open(path_out, 'wt') as file:\n",
" for r in results:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1ec57d97-a852-490e-8da4-d1e4c9676cd6",
"metadata": {},
"outputs": [],
"source": [
"def read_file(filename):\n",
" result = []\n",
" with open(filename, 'r', encoding=\"utf-8\") as file:\n",
" for line in file:\n",
" text = line.split(\"\\t\")[0].strip()\n",
" result.append(text)\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "86fbfb79-76e7-49f5-b722-2827f93cb03f",
"metadata": {},
"outputs": [],
"source": [
"with open('train/in.tsv', 'r', encoding='utf8') as file:\n",
" train = pd.read_csv(file, sep='\\t', header=None)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8960c975-f756-4e36-a1ce-e9fd5fdf8fe3",
"metadata": {},
"outputs": [],
"source": [
"with open('train/expected.tsv', 'r', encoding='utf8') as file:\n",
" train_y = pd.read_csv(file, sep='\\t', header=None)\n",
"train_y = train_y[0:10000]\n",
"train_y = train_y[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "07ae7b22-e95d-4614-9757-15660a9834b6",
"metadata": {},
"outputs": [],
"source": [
"train = train[0:10000]\n",
"train_x = train[0]\n",
"train_x = [gensim.utils.simple_preprocess(x) for x in train_x]\n",
"#train_x"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "fde71cd8-f682-4793-bce9-0f9a9d8c176c",
"metadata": {},
"outputs": [],
"source": [
"from gensim.test.utils import common_texts\n",
"from gensim.models import Word2Vec\n",
"\n",
"model = Word2Vec(sentences=train_x, vector_size=100, window=5, min_count=1, workers=4)\n",
"#data, min_count = 1, vector_size = 100, window = 5, sg = 1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9a4c8066-f985-478e-8944-dd45b73d9795",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\korne\\AppData\\Local\\Temp\\ipykernel_3520\\3800840358.py:2: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
" train_x_vec = np.array([np.array([model.wv[i] for i in x if i in words]) for x in train_x])\n"
]
}
],
"source": [
"words = set(model.wv.index_to_key)\n",
"train_x_vec = np.array([np.array([model.wv[i] for i in x if i in words]) for x in train_x])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b52269f9-f143-483d-9669-ce8f5972d6bb",
"metadata": {},
"outputs": [],
"source": [
"FEATURES = 100\n",
"\n",
"class NeuralNetworkModel(torch.nn.Module):\n",
" def __init__(self):\n",
" super(NeuralNetworkModel, self).__init__()\n",
" self.fc1 = torch.nn.Linear(FEATURES,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\n",
"\n",
"nn_model = NeuralNetworkModel()\n",
"BATCH_SIZE = 40\n",
"criterion = torch.nn.BCELoss()\n",
"optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)\n",
"\n",
"def get_loss_acc(model, data_x, data_y):\n",
" loss_score = 0\n",
" acc_score = 0\n",
" items_total = 0\n",
" model.eval()\n",
" for i in range(0, data_y.shape[0], BATCH_SIZE):\n",
" X = data_x[i:i+BATCH_SIZE]\n",
" X = torch.tensor(X.astype(np.float32))\n",
" Y = data_y[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]\n",
" return (loss_score / items_total), (acc_score / items_total)\n",
"\n",
"\n",
"for epoch in range(5):\n",
" loss_score = 0\n",
" acc_score = 0\n",
" items_total = 0\n",
" nn_model.train()\n",
" for i in range(0, train_y.shape[0] - 42, BATCH_SIZE):\n",
" X = train_x_vec[i:i+BATCH_SIZE]\n",
" X = torch.tensor(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_predictions = nn_model(X)\n",
" acc_score += torch.sum((Y_predictions > 0.5) == Y).item()\n",
" items_total += Y.shape[0]\n",
"\n",
" optimizer.zero_grad()\n",
" loss = criterion(Y_predictions, Y)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
"\n",
" loss_score += loss.item() * Y.shape[0]\n",
"\n",
" display(epoch)\n",
" display(get_loss_acc(model, train_x_vect, train_y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1482f342-f2ea-4c9d-b221-5ef451e3a6b3",
"metadata": {},
"outputs": [],
"source": [
"#print('trenowanie modelu')\n",
"model = NeuralNetworkModel()\n",
"BATCH_SIZE = 5\n",
"criterion = torch.nn.BCELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
"\n",
"for epoch in range(BATCH_SIZE):\n",
" model.train()\n",
" for i in range(0, y_train.shape[0], BATCH_SIZE):\n",
" X = x_train[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" y = y_train[i:i + BATCH_SIZE]\n",
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
" optimizer.zero_grad()\n",
" outputs = model(X.float())\n",
" loss = criterion(outputs, y)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
"#print('predykcja wynikow')\n",
"y_dev = []\n",
"y_test = []\n",
"model.eval()\n",
"\n",
"with torch.no_grad():\n",
" for i in range(0, len(x_dev), BATCH_SIZE):\n",
" X = x_dev[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" prediction = (outputs > 0.5)\n",
" y_dev += prediction.tolist()\n",
"\n",
" for i in range(0, len(x_test), BATCH_SIZE):\n",
" X = x_test[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" y = (outputs >= 0.5)\n",
" y_test += prediction.tolist()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "equal-singles",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
" LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\n",
"/usr/lib/python3/dist-packages/sklearn/feature_extraction/image.py:167: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" dtype=np.int):\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" eps=np.finfo(np.float).eps,\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" eps=np.finfo(np.float).eps, positive=False):\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" precompute=False, eps=np.finfo(np.float).eps,\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" eps=np.finfo(np.float).eps, random_state=None,\n",
"/usr/lib/python3/dist-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" eps=4 * np.finfo(np.float).eps, n_jobs=None,\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import csv\n",
"import lzma\n",
"import gensim.downloader\n",
"from nltk import word_tokenize"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "involved-understanding",
"metadata": {},
"outputs": [],
"source": [
"x_train = pd.read_table('in.tsv', sep='\\t', header=None, quoting=3)\n",
"y_train = pd.read_table('expected.tsv', sep='\\t', header=None, quoting=3)\n",
"#x_dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\\t', header=None, quoting=3)\n",
"#x_test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\\t', header=None, quoting=3)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "collaborative-cincinnati",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'torch' has no attribute 'nn'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-11c9482004ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#print('inicjalizacja modelu')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mNeuralNetworkModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNeuralNetworkModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml01\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'torch' has no attribute 'nn'"
]
}
],
"source": [
"#print('inicjalizacja modelu')\n",
"class NeuralNetworkModel(torch.nn.Module):\n",
" def __init__(self):\n",
" super(NeuralNetworkModel, self).__init__()\n",
" self.l01 = torch.nn.Linear(300, 300)\n",
" self.l02 = torch.nn.Linear(300, 1)\n",
"\n",
" def forward(self, x):\n",
" x = self.l01(x)\n",
" x = torch.relu(x)\n",
" x = self.l02(x)\n",
" x = torch.sigmoid(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "hydraulic-business",
"metadata": {},
"outputs": [],
"source": [
"#print('przygotowanie danych')\n",
"\n",
"x_train = x_train[0].str.lower()\n",
"y_train = y_train[0]\n",
"x_dev = x_dev[0].str.lower()\n",
"x_test = x_test[0].str.lower()\n",
"\n",
"x_train = [word_tokenize(x) for x in x_train]\n",
"x_dev = [word_tokenize(x) for x in x_dev]\n",
"x_test = [word_tokenize(x) for x in x_test]\n",
"\n",
"word2vec = gensim.downloader.load('word2vec-google-news-300')\n",
"x_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_train]\n",
"x_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_dev]\n",
"x_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_test]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "heavy-sandwich",
"metadata": {},
"outputs": [],
"source": [
"#print('trenowanie modelu')\n",
"model = NeuralNetworkModel()\n",
"BATCH_SIZE = 5\n",
"criterion = torch.nn.BCELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
"\n",
"for epoch in range(BATCH_SIZE):\n",
" model.train()\n",
" for i in range(0, y_train.shape[0], BATCH_SIZE):\n",
" X = x_train[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" y = y_train[i:i + BATCH_SIZE]\n",
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
" optimizer.zero_grad()\n",
" outputs = model(X.float())\n",
" loss = criterion(outputs, y)\n",
" loss.backward()\n",
" optimizer.step()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "small-pavilion",
"metadata": {},
"outputs": [],
"source": [
"#print('predykcja wynikow')\n",
"y_dev = []\n",
"y_test = []\n",
"model.eval()\n",
"\n",
"with torch.no_grad():\n",
" for i in range(0, len(x_dev), BATCH_SIZE):\n",
" X = x_dev[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" prediction = (outputs > 0.5)\n",
" y_dev += prediction.tolist()\n",
"\n",
" for i in range(0, len(x_test), BATCH_SIZE):\n",
" X = x_test[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" y = (outputs >= 0.5)\n",
" y_test += prediction.tolist()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "toxic-pendant",
"metadata": {},
"outputs": [],
"source": [
"# print('eksportowanie do plików')\n",
"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
"y_test = np.asarray(y_test, dtype=np.int32)\n",
"y_dev.tofile('./dev-0/out.tsv', sep='\\n')\n",
"y_test.tofile('./test-A/out.tsv', sep='\\n')\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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "74100403-147c-42cd-8285-e30778c0fb66",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import csv\n",
"import lzma\n",
"import gensim.downloader\n",
"from nltk import word_tokenize"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbe60d7b-850e-4838-b4ce-672f13bf2bb2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bf211ece-e27a-4119-a1b9-9a9a610cfb46",
"metadata": {},
"outputs": [],
"source": [
"def predict_year(x, path_out, model):\n",
" results = model.predict(x)\n",
" with open(path_out, 'wt') as file:\n",
" for r in results:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1ec57d97-a852-490e-8da4-d1e4c9676cd6",
"metadata": {},
"outputs": [],
"source": [
"def read_file(filename):\n",
" result = []\n",
" with open(filename, 'r', encoding=\"utf-8\") as file:\n",
" for line in file:\n",
" text = line.split(\"\\t\")[0].strip()\n",
" result.append(text)\n",
" return result"
]
},
{
"cell_type": "code",
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"id": "86fbfb79-76e7-49f5-b722-2827f93cb03f",
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"... ... ...\n",
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"199996 I don't mean it in a dickish way, I'm being se... 1322700456\n",
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"199998 Right. Scientists tend to think and conclude l... 1348777201\n",
"199999 Because they are illiterate 1249579722\n",
"\n",
"[200000 rows x 2 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train = pd.read_table('train/in.tsv', sep='\\t', header=None, quoting=3)\n",
"x_train = x_train[0:200000]\n",
"x_train"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8960c975-f756-4e36-a1ce-e9fd5fdf8fe3",
"metadata": {},
"outputs": [
{
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"with open('train/expected.tsv', 'r', encoding='utf8') as file:\n",
" y_train = pd.read_csv(file, sep='\\t', header=None)\n",
"y_train = y_train[0:200000]\n",
"y_train"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6b27e6ce-e9fd-41a1-aacf-53a5fde0a7c1",
"metadata": {},
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{
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" <tr>\n",
" <th>5268</th>\n",
" <td>EVP....that's a shot in the GH drinking game.</td>\n",
" <td>1354408646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5269</th>\n",
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],
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" 0 1\n",
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"3 An example would have been more productive tha... 1315584834\n",
"4 sorry, but the authors of this article admit t... 1347389166\n",
"... ... ...\n",
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"5271 Tampering of candy? More like cooking somethin... 1320262659\n",
"\n",
"[5272 rows x 2 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('dev-0/in.tsv', 'r', encoding='utf8') as file:\n",
" x_dev = pd.read_csv(file, sep='\\t', header=None)\n",
"x_dev"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "99ae526d-9b7c-493f-be4f-f95b1c8f4b81",
"metadata": {},
"outputs": [
{
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" <td>GAMBIT</td>\n",
" <td>1326441107</td>\n",
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" <tr>\n",
" <th>5148</th>\n",
" <td>&amp;gt;Joe Rogan is no snake oil salesman.\\n\\nHe ...</td>\n",
" <td>1319464245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5149</th>\n",
" <td>Reading further, Sagan does seem to agree with...</td>\n",
" <td>1322126150</td>\n",
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" <tr>\n",
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],
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" 0 1\n",
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"... ... ...\n",
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"\n",
"[5152 rows x 2 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('test-A/in.tsv', 'r', encoding='utf8') as file:\n",
" x_test = pd.read_csv(file, sep='\\t', header=None)\n",
"x_test"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "dba17668-971f-47f8-99ce-fc840b5cb74a",
"metadata": {},
"outputs": [],
"source": [
"class NeuralNetworkModel(torch.nn.Module):\n",
" def __init__(self):\n",
" super(NeuralNetworkModel, self).__init__()\n",
" self.l01 = torch.nn.Linear(300, 300)\n",
" self.l02 = torch.nn.Linear(300, 1)\n",
"\n",
" def forward(self, x):\n",
" x = self.l01(x)\n",
" x = torch.relu(x)\n",
" x = self.l02(x)\n",
" x = torch.sigmoid(x)\n",
" return x\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1a275c1d-75bc-4290-9332-56396d16a0f2",
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train[0].str.lower()\n",
"y_train = y_train[0]\n",
"x_dev = x_dev[0].str.lower()\n",
"x_test = x_test[0].str.lower()\n",
"\n",
"x_train = [word_tokenize(x) for x in x_train]\n",
"x_dev = [word_tokenize(x) for x in x_dev]\n",
"x_test = [word_tokenize(x) for x in x_test]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "031a3670-3be7-4146-97b4-0dacd4f9ae58",
"metadata": {},
"outputs": [],
"source": [
"from gensim.test.utils import common_texts\n",
"from gensim.models import Word2Vec\n",
"\n",
"word2vec = gensim.downloader.load('word2vec-google-news-300')\n",
"x_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_train]\n",
"x_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_dev]\n",
"x_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_test]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b7defd18-e281-4cf6-9941-cee560749677",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\korne\\AppData\\Local\\Temp\\ipykernel_22024\\3484013121.py:10: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\torch\\csrc\\utils\\tensor_new.cpp:210.)\n",
" X = torch.tensor(X)\n"
]
}
],
"source": [
"model = NeuralNetworkModel()\n",
"BATCH_SIZE = 5\n",
"criterion = torch.nn.BCELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
"\n",
"for epoch in range(BATCH_SIZE):\n",
" model.train()\n",
" for i in range(0, y_train.shape[0], BATCH_SIZE):\n",
" X = x_train[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" y = y_train[i:i + BATCH_SIZE]\n",
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
" optimizer.zero_grad()\n",
" outputs = model(X.float())\n",
" loss = criterion(outputs, y)\n",
" loss.backward()\n",
" optimizer.step()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "92c69ddd-fe58-477f-b2c2-06324a983bcc",
"metadata": {},
"outputs": [],
"source": [
"y_dev = []\n",
"y_test = []\n",
"model.eval()\n",
"\n",
"with torch.no_grad():\n",
" for i in range(0, len(x_dev), BATCH_SIZE):\n",
" X = x_dev[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" prediction = (outputs > 0.5)\n",
" y_dev += prediction.tolist()\n",
"\n",
" for i in range(0, len(x_test), BATCH_SIZE):\n",
" X = x_test[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" y = (outputs >= 0.5)\n",
" y_test += prediction.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "caff921c-d0ab-4fce-a17f-6610266b404d",
"metadata": {},
"outputs": [],
"source": [
"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
"y_test = np.asarray(y_test, dtype=np.int32)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "73076eb2-810f-4f85-aa3f-05ee884c413b",
"metadata": {},
"outputs": [],
"source": [
"with open('./dev-0/out.tsv', 'wt') as file:\n",
" for r in y_dev:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ddda251c-cafa-40f8-a020-48310a9f23b6",
"metadata": {},
"outputs": [],
"source": [
"with open('./test-A/out.tsv', 'wt') as file:\n",
" for r in y_test:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "5730562a-0200-4c8f-8f73-992fa2b36133",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook run.ipynb to script\n",
"[NbConvertApp] Writing 3816 bytes to run.py\n"
]
}
],
"source": [
"!jupyter nbconvert --to script run.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07a09298-204c-4905-90a8-5dcb87877368",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
import torch
import csv
import lzma
import gensim.downloader
from nltk import word_tokenize
# In[ ]:
# In[2]:
def predict_year(x, path_out, model):
results = model.predict(x)
with open(path_out, 'wt') as file:
for r in results:
file.write(str(r) + '\n')
# In[3]:
def read_file(filename):
result = []
with open(filename, 'r', encoding="utf-8") as file:
for line in file:
text = line.split("\t")[0].strip()
result.append(text)
return result
# In[4]:
x_train = pd.read_table('train/in.tsv', sep='\t', header=None, quoting=3)
x_train = x_train[0:200000]
x_train
# In[5]:
with open('train/expected.tsv', 'r', encoding='utf8') as file:
y_train = pd.read_csv(file, sep='\t', header=None)
y_train = y_train[0:200000]
y_train
# In[6]:
with open('dev-0/in.tsv', 'r', encoding='utf8') as file:
x_dev = pd.read_csv(file, sep='\t', header=None)
x_dev
# In[7]:
with open('test-A/in.tsv', 'r', encoding='utf8') as file:
x_test = pd.read_csv(file, sep='\t', header=None)
x_test
# In[8]:
class NeuralNetworkModel(torch.nn.Module):
def __init__(self):
super(NeuralNetworkModel, self).__init__()
self.l01 = torch.nn.Linear(300, 300)
self.l02 = torch.nn.Linear(300, 1)
def forward(self, x):
x = self.l01(x)
x = torch.relu(x)
x = self.l02(x)
x = torch.sigmoid(x)
return x
# In[9]:
x_train = x_train[0].str.lower()
y_train = y_train[0]
x_dev = x_dev[0].str.lower()
x_test = x_test[0].str.lower()
x_train = [word_tokenize(x) for x in x_train]
x_dev = [word_tokenize(x) for x in x_dev]
x_test = [word_tokenize(x) for x in x_test]
# In[11]:
from gensim.test.utils import common_texts
from gensim.models import Word2Vec
word2vec = gensim.downloader.load('word2vec-google-news-300')
x_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_train]
x_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_dev]
x_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_test]
# In[ ]:
model = NeuralNetworkModel()
BATCH_SIZE = 5
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
for epoch in range(BATCH_SIZE):
model.train()
for i in range(0, y_train.shape[0], BATCH_SIZE):
X = x_train[i:i + BATCH_SIZE]
X = torch.tensor(X)
y = y_train[i:i + BATCH_SIZE]
y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
optimizer.zero_grad()
outputs = model(X.float())
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
# In[ ]:
y_dev = []
y_test = []
model.eval()
with torch.no_grad():
for i in range(0, len(x_dev), BATCH_SIZE):
X = x_dev[i:i + BATCH_SIZE]
X = torch.tensor(X)
outputs = model(X.float())
prediction = (outputs > 0.5)
y_dev += prediction.tolist()
for i in range(0, len(x_test), BATCH_SIZE):
X = x_test[i:i + BATCH_SIZE]
X = torch.tensor(X)
outputs = model(X.float())
y = (outputs >= 0.5)
y_test += prediction.tolist()
# In[ ]:
y_dev = np.asarray(y_dev, dtype=np.int32)
y_test = np.asarray(y_test, dtype=np.int32)
# In[ ]:
with open('./dev-0/out.tsv', 'wt') as file:
for r in y_dev:
file.write(str(r) + '\n')
# In[ ]:
with open('./test-A/out.tsv', 'wt') as file:
for r in y_test:
file.write(str(r) + '\n')
# In[ ]:
get_ipython().system('jupyter nbconvert --to script run.ipynb')

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "equal-singles",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import csv\n",
"import lzma\n",
"import gensim.downloader\n",
"from nltk import word_tokenize"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "involved-understanding",
"metadata": {},
"outputs": [],
"source": [
"x_train = pd.read_table('in.tsv', sep='\\t', header=None, quoting=3)\n",
"y_train = pd.read_table('expected.tsv', sep='\\t', header=None, quoting=3)\n",
"#x_dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\\t', header=None, quoting=3)\n",
"#x_test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\\t', header=None, quoting=3)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "collaborative-cincinnati",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "module 'torch' has no attribute 'nn'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-11c9482004ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#print('inicjalizacja modelu')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mNeuralNetworkModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNeuralNetworkModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml01\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'torch' has no attribute 'nn'"
]
}
],
"source": [
"#print('inicjalizacja modelu')\n",
"class NeuralNetworkModel(torch.nn.Module):\n",
" def __init__(self):\n",
" super(NeuralNetworkModel, self).__init__()\n",
" self.l01 = torch.nn.Linear(300, 300)\n",
" self.l02 = torch.nn.Linear(300, 1)\n",
"\n",
" def forward(self, x):\n",
" x = self.l01(x)\n",
" x = torch.relu(x)\n",
" x = self.l02(x)\n",
" x = torch.sigmoid(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "hydraulic-business",
"metadata": {},
"outputs": [],
"source": [
"#print('przygotowanie danych')\n",
"\n",
"x_train = x_train.str.lower()\n",
"x_dev = x_dev[0].str.lower()\n",
"x_test = x_test[0].str.lower()\n",
"\n",
"x_train = [word_tokenize(x) for x in x_train]\n",
"x_dev = [word_tokenize(x) for x in x_dev]\n",
"x_test = [word_tokenize(x) for x in x_test]\n",
"\n",
"word2vec = gensim.downloader.load('word2vec-google-news-300')\n",
"x_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_train]\n",
"x_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_dev]\n",
"x_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [np.zeros(300)], axis=0) for content in x_test]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "heavy-sandwich",
"metadata": {},
"outputs": [],
"source": [
"#print('trenowanie modelu')\n",
"model = NeuralNetworkModel()\n",
"BATCH_SIZE = 5\n",
"criterion = torch.nn.BCELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
"\n",
"for epoch in range(BATCH_SIZE):\n",
" model.train()\n",
" for i in range(0, y_train.shape[0], BATCH_SIZE):\n",
" X = x_train[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" y = y_train[i:i + BATCH_SIZE]\n",
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
" optimizer.zero_grad()\n",
" outputs = model(X.float())\n",
" loss = criterion(outputs, y)\n",
" loss.backward()\n",
" optimizer.step()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "small-pavilion",
"metadata": {},
"outputs": [],
"source": [
"#print('predykcja wynikow')\n",
"y_dev = []\n",
"y_test = []\n",
"model.eval()\n",
"\n",
"with torch.no_grad():\n",
" for i in range(0, len(x_dev), BATCH_SIZE):\n",
" X = x_dev[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" prediction = (outputs > 0.5)\n",
" y_dev += prediction.tolist()\n",
"\n",
" for i in range(0, len(x_test), BATCH_SIZE):\n",
" X = x_test[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" y = (outputs >= 0.5)\n",
" y_test += prediction.tolist()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "toxic-pendant",
"metadata": {},
"outputs": [],
"source": [
"# print('eksportowanie do plików')\n",
"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
"y_test = np.asarray(y_test, dtype=np.int32)\n",
"y_dev.tofile('./dev-0/out.tsv', sep='\\n')\n",
"y_test.tofile('./test-A/out.tsv', sep='\\n')\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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