From dda026998dba9694c513c5464b848617e75beb66 Mon Sep 17 00:00:00 2001 From: jcollopy-tulane Date: Thu, 25 Apr 2024 21:00:14 -0500 Subject: [PATCH] Adding Experiment --- notebooks/Experiment-CNN.ipynb | 187 +++++++++++++++++++++++++++++++++ notebooks/NLP_Project.ipynb | 4 +- 2 files changed, 189 insertions(+), 2 deletions(-) create mode 100644 notebooks/Experiment-CNN.ipynb diff --git a/notebooks/Experiment-CNN.ipynb b/notebooks/Experiment-CNN.ipynb new file mode 100644 index 0000000..0aa3a7f --- /dev/null +++ b/notebooks/Experiment-CNN.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "70f2a5ec-67f6-4d3c-b912-717e401fc70e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import keras as keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Embedding, Flatten, Dense\n", + "from sklearn.model_selection import GridSearchCV\n", + "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n", + "from tensorflow.keras.optimizers import Adam, RMSprop\n", + "from keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.metrics import classification_report\n", + "from tensorflow.keras import layers\n", + "from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, Dropout" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7acbbe88-4550-41d6-b0bb-ede002bc4e0b", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'train.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrain.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m val_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidation.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3\u001b[0m test_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 1014\u001b[0m dialect,\n\u001b[1;32m 1015\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 1023\u001b[0m )\n\u001b[1;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[0;32m/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m 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\u001b[0;32m/opt/anaconda3/envs/testenv/lib/python3.9/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'train.csv'" + ] + } + ], + "source": [ + "train_df = pd.read_csv(\"train.csv\")\n", + "val_df = pd.read_csv(\"validation.csv\")\n", + "test_df = pd.read_csv(\"test.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1eac01e8-3c62-469b-96ea-d4babd8f9348", + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = Tokenizer()\n", + "\n", + "tokenizer.fit_on_texts(train_df[\"Comment_Adj\"])\n", + "tokenizer.fit_on_texts(val_df[\"Comment_Adj\"])\n", + "X_train = tokenizer.texts_to_sequences(train_df[\"Comment_Adj\"])\n", + "X_val = tokenizer.texts_to_sequences(val_df[\"Comment_Adj\"])\n", + "\n", + "vocab_size = len(tokenizer.word_index) + 1\n", + "\n", + "maxlen = 100\n", + "X_train = pad_sequences(X_train, padding='post', maxlen=maxlen)\n", + "X_val = pad_sequences(X_val, padding='post', maxlen=maxlen)\n", + "label_encoder = LabelEncoder()\n", + "y_train = label_encoder.fit_transform(train_df[\"Result_Bin\"])\n", + "y_val = label_encoder.fit_transform(val_df[\"Result_Bin\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f61ab0b-4594-456c-9b22-440cf9c5fabe", + "metadata": {}, + "outputs": [], + "source": [ + "def set_all_seeds(seed_value):\n", + " random.seed(seed_value)\n", + " np.random.seed(seed_value)\n", + " tf.random.set_seed(seed_value)\n", + "\n", + "set_all_seeds(42)\n", + "\n", + "def CNN_model_adj(embedding=200, filter=16, kernel=4, num_1=40, lr=0.01, dropout_rate=0.5):\n", + " set_all_seeds(42)\n", + " model = Sequential()\n", + " model.add(layers.Embedding(input_dim=vocab_size, \n", + " output_dim=embedding, \n", + " input_length=maxlen))\n", + " model.add(Conv1D(filters=filter, kernel_size=kernel, activation=\"relu\"))\n", + " model.add(MaxPooling1D(pool_size=2))\n", + " model.add(layers.Flatten())\n", + " model.add(layers.Dropout(dropout_rate))\n", + " model.add(layers.Dense(num_1, activation='relu', kernel_regularizer=l2(0.001)))\n", + " model.add(layers.Dense(1, activation='sigmoid'))\n", + " model.compile(optimizer=Adam(learning_rate=lr),\n", + " loss='binary_crossentropy',\n", + " metrics=['accuracy'])\n", + " return model\n", + "\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "\n", + "\n", + "filters = [16, 32, 48]\n", + "kernels = [4, 6]\n", + "num_1 = [40, 70, 100]\n", + "lrs = [0.01, 0.001]\n", + "dropout_rate = [0.5, 0.6]\n", + "\n", + "best_accuracy = 0\n", + "best_history = None\n", + "best_model_desc = \"\"\n", + "best_model_cnn = None\n", + "\n", + "for filter in filters:\n", + " for kernel in kernels:\n", + " for num in num_1:\n", + " for lr in lrs:\n", + " for rate in dropout_rate:\n", + " model_desc = f\"filter = {filter}, kernel = {kernel}, num_1 = {num}, lr = {lr}, dropout_rate = {rate}\"\n", + " model = CNN_model_adj(embedding = 200, filter = filter, kernel = kernel, num_1 = num, lr = lr, dropout_rate = rate)\n", + " history = model.fit(X_train, y_train,\n", + " epochs=30,\n", + " verbose=False,\n", + " validation_data=(X_val, y_val),\n", + " batch_size=1000, callbacks = [early_stopping])\n", + " val_loss, val_accuracy = model.evaluate(X_val, y_val, verbose=False)\n", + "\n", + " if val_accuracy > best_accuracy:\n", + " best_accuracy = val_accuracy\n", + " best_history = history\n", + " best_model_desc = model_desc\n", + " best_model_cnn = model\n", + "\n", + "\n", + "print(f\"The best model has parameters: {best_model_desc} with accuracy = {round(best_accuracy, 4)}.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cf60181-88fc-48dd-be73-a19222236939", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cfa7100-4fed-4538-b8ce-213a5efb4d1f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (testenv)", + "language": "python", + "name": "testenv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/NLP_Project.ipynb b/notebooks/NLP_Project.ipynb index 12d395d..24df0f7 100644 --- a/notebooks/NLP_Project.ipynb +++ b/notebooks/NLP_Project.ipynb @@ -4544,7 +4544,7 @@ ], "metadata": { "kernelspec": { - "display_name": "testenv", + "display_name": "Python (testenv)", "language": "python", "name": "testenv" }, @@ -4558,7 +4558,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.18" } }, "nbformat": 4,