NN with on value

.gitignore

@@ -7,3 +7,4 @@ data_train.csv
 data.csv 
 data_not_shuf.csv
 data_not_cutted.csv
+venv

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,34 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="7">
+            <item index="0" class="java.lang.String" itemvalue="pl-core-news-sm" />
+            <item index="1" class="java.lang.String" itemvalue="en-core-web-sm" />
+            <item index="2" class="java.lang.String" itemvalue="livocat-core" />
+            <item index="3" class="java.lang.String" itemvalue="tqdm" />
+            <item index="4" class="java.lang.String" itemvalue="spacy" />
+            <item index="5" class="java.lang.String" itemvalue="streamlit" />
+            <item index="6" class="java.lang.String" itemvalue="requests" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+        </list>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="translation_handler.fairseq_translation.FairseqTransferer" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/ium_444463.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (ium_444463)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (ium_444463)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/ium_444463.iml" filepath="$PROJECT_DIR$/.idea/ium_444463.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

main.py

@@ -0,0 +1,113 @@
+import pandas as pd
+import numpy as np
+import scipy
+import torch
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import kaggle
+from sklearn.feature_extraction.text import TfidfVectorizer
+from torch import nn
+from torch import optim
+import matplotlib.pyplot as plt
+
+if __name__ == "__main__":
+    # kaggle.api.authenticate()
+    # kaggle.api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', path='.',
+    #                                   unzip=True)
+
+    data = pd.read_csv('fake_job_postings.csv', engine='python')
+    data = data.replace(np.nan, '', regex=True)
+
+    data_train, data_test = train_test_split(data, test_size=3000, random_state=1)
+    data_dev, data_test = train_test_split(data_test, test_size=1500, random_state=1)
+
+    x_train = data_train["title"]
+    x_dev = data_dev["title"]
+    x_test = data_test["title"]
+
+    y_train = data_train["fraudulent"]
+    y_dev = data_dev["fraudulent"]
+    y_test = data_test["fraudulent"]
+
+    x_train = np.array(x_train)
+    x_dev = np.array(x_dev)
+
+    y_train = np.array(y_train)
+    y_dev = np.array(y_dev)
+
+    vectorizer = TfidfVectorizer()
+
+    x_train = vectorizer.fit_transform(x_train)
+    x_dev = vectorizer.transform(x_dev)
+
+    x_train = torch.tensor(scipy.sparse.csr_matrix.todense(x_train)).float()
+    x_dev = torch.tensor(scipy.sparse.csr_matrix.todense(x_dev)).float()
+
+    y_train = torch.tensor(y_train)
+    y_dev = torch.tensor(y_dev)
+
+    from torch import nn
+
+    model = nn.Sequential(
+        nn.Linear(x_train.shape[1], 64),
+        nn.ReLU(),
+        nn.Linear(64, data_train["title"].nunique()),
+        nn.LogSoftmax(dim=1))
+
+    # Define the loss
+    criterion = nn.NLLLoss()  # Forward pass, log
+    logps = model(x_train)  # Calculate the loss with the logits and the labels
+    loss = criterion(logps, y_train)
+    loss.backward()  # Optimizers need parameters to optimize and a learning rate
+    optimizer = optim.Adam(model.parameters(), lr=0.002)
+
+    train_losses = []
+    test_losses = []
+    test_accuracies = []
+
+    epochs = 5
+    for e in range(epochs):
+        optimizer.zero_grad()
+
+        output = model.forward(x_train)
+        loss = criterion(output, y_train)
+        loss.backward()
+        train_loss = loss.item()
+        train_losses.append(train_loss)
+
+        optimizer.step()
+
+        # Turn off gradients for validation, saves memory and computations
+        with torch.no_grad():
+            model.eval()
+            log_ps = model(x_dev)
+            test_loss = criterion(log_ps, y_dev)
+            test_losses.append(test_loss)
+
+            ps = torch.exp(log_ps)
+            top_p, top_class = ps.topk(1, dim=1)
+            equals = top_class == y_dev.view(*top_class.shape)
+            test_accuracy = torch.mean(equals.float())
+            test_accuracies.append(test_accuracy)
+
+        model.train()
+
+        print(f"Epoch: {e + 1}/{epochs}.. ",
+              f"Training Loss: {train_loss:.3f}.. ",
+              f"Test Loss: {test_loss:.3f}.. ",
+              f"Test Accuracy: {test_accuracy:.3f}")
+
+    plt.figure(figsize=(12, 5))
+    ax = plt.subplot(121)
+    plt.xlabel('epochs')
+    plt.ylabel('negative log likelihood loss')
+    plt.plot(train_losses, label='Training loss')
+    plt.plot(test_losses, label='Validation loss')
+    plt.legend(frameon=False)
+    plt.subplot(122)
+    plt.xlabel('epochs')
+    plt.ylabel('test accuracy')
+    plt.plot(test_accuracies)
+    plt.show()
+
+    print('Succes')

requirements.txt

@@ -1,3 +1,7 @@
 pandas
 numpy 
 kaggle
+torch
+matplotlib
+sklearn
+scipy

stare_zadania/download_data.ipynb

@@ -4,7 +4,11 @@
    "cell_type": "code",
    "execution_count": 28,
    "id": "5e2107a5",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "#Skrypt do ściagnięcia zbiory danych\n"
@@ -14,7 +18,11 @@
    "cell_type": "code",
    "execution_count": 29,
    "id": "bcc889e5",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -29,14 +37,14 @@
       "Requirement already satisfied: python-slugify in /home/students/s444463/.local/lib/python3.8/site-packages (from kaggle) (6.1.1)\n",
       "Requirement already satisfied: python-dateutil in /usr/lib/python3/dist-packages (from kaggle) (2.7.3)\n",
       "Requirement already satisfied: text-unidecode>=1.3 in /home/students/s444463/.local/lib/python3.8/site-packages (from python-slugify->kaggle) (1.3)\n",
-      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
-      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "\u001B[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
+      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001B[0m\n",
       "Requirement already satisfied: pandas in /usr/lib/python3/dist-packages (0.25.3)\n",
-      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
-      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "\u001B[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
+      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001B[0m\n",
       "Requirement already satisfied: numpy in /usr/lib/python3/dist-packages (1.17.4)\n",
-      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
-      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
+      "\u001B[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
+      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001B[0m\n"
      ]
     }
    ],
@@ -50,7 +58,11 @@
    "cell_type": "code",
    "execution_count": 30,
    "id": "02a4034f",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -70,7 +82,11 @@
    "cell_type": "code",
    "execution_count": 31,
    "id": "5035aef0",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -88,7 +104,11 @@
    "cell_type": "code",
    "execution_count": 32,
    "id": "14344d2f",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -105,8 +125,8 @@
       "Requirement already satisfied: kiwisolver>=1.0.1 in /home/students/s444463/.local/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
       "Requirement already satisfied: python-dateutil>=2.7 in /usr/lib/python3/dist-packages (from matplotlib>=2.2->seaborn) (2.7.3)\n",
       "Requirement already satisfied: six in /usr/lib/python3/dist-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.14.0)\n",
-      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
-      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
+      "\u001B[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
+      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001B[0m\n"
      ]
     }
    ],
@@ -118,7 +138,11 @@
    "cell_type": "code",
    "execution_count": 33,
    "id": "0f5ebfab",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -534,7 +558,11 @@
    "cell_type": "code",
    "execution_count": 34,
    "id": "edbf49da",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -553,7 +581,11 @@
    "cell_type": "code",
    "execution_count": 35,
    "id": "e60b3f32",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -585,7 +617,11 @@
    "cell_type": "code",
    "execution_count": 36,
    "id": "ddb2fc38",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1001,7 +1037,11 @@
    "cell_type": "code",
    "execution_count": 37,
    "id": "c5ac75f5",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1373,7 +1413,11 @@
    "cell_type": "code",
    "execution_count": 38,
    "id": "4b0e77a4",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1399,7 +1443,11 @@
    "cell_type": "code",
    "execution_count": 39,
    "id": "5a1d8ec7",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1411,8 +1459,8 @@
       "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/students/s444463/.local/lib/python3.8/site-packages (from scikit-learn) (3.1.0)\n",
       "Requirement already satisfied: joblib>=0.11 in /usr/lib/python3/dist-packages (from scikit-learn) (0.14.0)\n",
       "Requirement already satisfied: scipy>=1.1.0 in /usr/lib/python3/dist-packages (from scikit-learn) (1.3.3)\n",
-      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
-      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "\u001B[33mWARNING: You are using pip version 21.2.4; however, version 22.0.4 is available.\n",
+      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001B[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -1425,7 +1473,11 @@
    "cell_type": "code",
    "execution_count": 40,
    "id": "50813795",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1461,7 +1513,11 @@
    "cell_type": "code",
    "execution_count": 41,
    "id": "ea3c9f2e",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1483,7 +1539,11 @@
    "cell_type": "code",
    "execution_count": 42,
    "id": "b20cc27a",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {

stare_zadania/requirements.txt

@@ -0,0 +1,3 @@
+pandas
+numpy 
+kaggle