From 3bcce61a462340d9782f238018fcf69381a9d19e Mon Sep 17 00:00:00 2001
From: Jakub Adamski <jadamski@match-trade.com>
Date: Wed, 14 Jun 2023 10:59:08 +0200
Subject: [PATCH] SE8 fix

---
 .gitignore |   4 +-
 SE8.ipynb  | 291 +++++++++++++++++++++++++++++++++++++++++++++++------
 data.csv   |  81 +++++++++++++++
 3 files changed, 343 insertions(+), 33 deletions(-)
 create mode 100644 data.csv

diff --git a/.gitignore b/.gitignore
index 4d4a299..ca1dcf4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,3 @@
-.conda/
\ No newline at end of file
+.conda/
+output/
+*.mp4
\ No newline at end of file
diff --git a/SE8.ipynb b/SE8.ipynb
index d13903b..7ef0845 100644
--- a/SE8.ipynb
+++ b/SE8.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
@@ -32,6 +33,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -50,6 +52,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -78,6 +81,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -125,6 +129,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -150,6 +155,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -163,6 +169,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -172,6 +179,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -179,6 +187,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -195,6 +204,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -211,6 +221,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -218,6 +229,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -257,6 +269,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -281,6 +294,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -433,6 +447,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -440,6 +455,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -447,11 +463,17 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "from fer import FER, Video\n",
+    "import cv2"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -459,11 +481,17 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "input_video_path = \"face.mp4\"\n",
+    "output_video_path = \"out.mp4\""
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -471,11 +499,16 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "video = Video(input_video_path)"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -483,11 +516,31 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:fer:30.00 fps, 80 frames, 2.67 seconds\n",
+      "INFO:fer:Making directories at output\n",
+      "100%|██████████| 80/80 [00:06<00:00, 11.67frames/s]\n",
+      "INFO:fer:Completed analysis: saved to output\\face_output.mp4\n",
+      "INFO:fer:Starting to Zip\n",
+      "INFO:fer:Compressing: 62%\n",
+      "INFO:fer:Zip has finished\n"
+     ]
+    }
+   ],
+   "source": [
+    "detector = FER(mtcnn=True)\n",
+    "analysis = video.analyze(detector, display=False)"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -495,11 +548,24 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'box0': [72, 192, 207, 269], 'angry0': 0.19, 'disgust0': 0.0, 'fear0': 0.06, 'happy0': 0.0, 'sad0': 0.17, 'surprise0': 0.0, 'neutral0': 0.58}, {'box0': [72, 192, 208, 269], 'angry0': 0.14, 'disgust0': 0.0, 'fear0': 0.04, 'happy0': 0.0, 'sad0': 0.14, 'surprise0': 0.0, 'neutral0': 0.68}, {'box0': [71, 191, 208, 271], 'angry0': 0.2, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.15, 'surprise0': 0.0, 'neutral0': 0.62}, {'box0': [71, 192, 208, 267], 'angry0': 0.24, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.15, 'surprise0': 0.0, 'neutral0': 0.59}, {'box0': [72, 193, 206, 266], 'angry0': 0.28, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.13, 'surprise0': 0.0, 'neutral0': 0.56}, {'box0': [69, 189, 209, 265], 'angry0': 0.33, 'disgust0': 0.0, 'fear0': 0.04, 'happy0': 0.0, 'sad0': 0.11, 'surprise0': 0.0, 'neutral0': 0.51}, {'box0': [70, 191, 205, 262], 'angry0': 0.3, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.12, 'surprise0': 0.0, 'neutral0': 0.56}, {'box0': [69, 190, 206, 263], 'angry0': 0.25, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.11, 'surprise0': 0.0, 'neutral0': 0.62}, {'box0': [69, 190, 206, 262], 'angry0': 0.22, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.14, 'surprise0': 0.0, 'neutral0': 0.62}, {'box0': [73, 189, 203, 264], 'angry0': 0.17, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.14, 'surprise0': 0.0, 'neutral0': 0.67}, {'box0': [72, 188, 203, 262], 'angry0': 0.17, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.1, 'surprise0': 0.0, 'neutral0': 0.7}, {'box0': [72, 187, 202, 263], 'angry0': 0.16, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.11, 'surprise0': 0.0, 'neutral0': 0.7}, {'box0': [72, 187, 204, 263], 'angry0': 0.12, 'disgust0': 0.0, 'fear0': 0.02, 'happy0': 0.0, 'sad0': 0.08, 'surprise0': 0.0, 'neutral0': 0.78}, {'box0': [72, 186, 204, 263], 'angry0': 0.1, 'disgust0': 0.0, 'fear0': 0.03, 'happy0': 0.0, 'sad0': 0.09, 'surprise0': 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0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [64, 173, 210, 271], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [66, 174, 209, 271], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [67, 174, 208, 270], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [66, 173, 211, 271], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [62, 172, 224, 271], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [65, 171, 216, 276], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [62, 172, 224, 270], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [60, 172, 224, 272], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [67, 173, 214, 274], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [60, 172, 223, 272], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [61, 171, 224, 270], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [62, 172, 223, 270], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [66, 170, 213, 275], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [59, 171, 225, 270], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [61, 172, 223, 269], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [60, 172, 223, 270], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [61, 171, 223, 270], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [64, 165, 216, 280], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [60, 170, 222, 271], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.99, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.01}, {'box0': [63, 172, 221, 270], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 1.0, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [66, 174, 209, 269], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.98, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.02}, {'box0': [70, 178, 211, 264], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.93, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.07}, {'box0': [71, 181, 209, 266], 'angry0': 0.01, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.58, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.4}, {'box0': [73, 186, 198, 251], 'angry0': 0.03, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.06, 'sad0': 0.01, 'surprise0': 0.0, 'neutral0': 0.89}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analysis)"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -507,6 +573,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -516,11 +583,22 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import soundfile\n",
+    "import numpy as np\n",
+    "import librosa\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -528,11 +606,32 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "def extract_features(file_name, mfcc, chroma, mel):\n",
+    "    with soundfile.SoundFile(file_name) as sound_file:\n",
+    "        X = sound_file.read(dtype=\"float32\")\n",
+    "        sample_rate = sound_file.samplerate\n",
+    "        if chroma:\n",
+    "            stft = np.abs(librosa.stft(X))\n",
+    "        result = np.array([])\n",
+    "        if mfcc:\n",
+    "            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
+    "            result = np.hstack((result, mfccs))\n",
+    "        if chroma:\n",
+    "            chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)\n",
+    "            result = np.hstack((result, chroma))\n",
+    "        if mel:\n",
+    "            mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate).T, axis=0)\n",
+    "            result = np.hstack((result, mel))\n",
+    "    return result\n"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -540,11 +639,20 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "emotions = {\n",
+    "    '03': 'happy',\n",
+    "    '04': 'sad',\n",
+    "    '05': 'angry'\n",
+    "}"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -552,11 +660,44 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "def load_data(test_size=0.2):\n",
+    "    x, y = [], []\n",
+    "    main_data_folder = \"C:\\\\Users\\\\jadamski\\\\Downloads\\\\speech-emotion-recognition-ravdess-data\"\n",
+    "    \n",
+    "\n",
+    "    # Iterate through each actor's folder\n",
+    "    for actor_folder in os.listdir(main_data_folder):\n",
+    "        actor_folder_path = os.path.join(main_data_folder, actor_folder)\n",
+    "\n",
+    "        # Ensure it's a directory\n",
+    "        if os.path.isdir(actor_folder_path):\n",
+    "            \n",
+    "            # Iterate through each file inside the actor's folder\n",
+    "            for file in os.listdir(actor_folder_path):\n",
+    "                emotion_code = file.split(\"-\")[2]\n",
+    "                \n",
+    "                # Process only files with the emotions you're interested in\n",
+    "                if emotion_code in emotions:\n",
+    "                    file_name = os.path.join(actor_folder_path, file)\n",
+    "                    emotion = emotions[emotion_code]\n",
+    "                    \n",
+    "                    # Extract features from the audio file\n",
+    "                    feature = extract_features(file_name, mfcc=True, chroma=True, mel=True)\n",
+    "                    \n",
+    "                    # Append features and emotion to the lists\n",
+    "                    x.append(feature)\n",
+    "                    y.append(emotion)\n",
+    "                \n",
+    "    return train_test_split(np.array(x), y, test_size=test_size, random_state=9)\n"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -564,11 +705,16 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = load_data(test_size=0.25)"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -576,11 +722,24 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Treningowe: 432, Testowe: 144\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Treningowe: {x_train.shape[0]}, Testowe: {x_test.shape[0]}')"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -588,11 +747,24 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Liczba cech: 180\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Liczba cech: {x_train.shape[1]}')\n"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -600,11 +772,16 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "model = MLPClassifier(alpha=0.01, batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive', max_iter=500)"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -612,11 +789,42 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:py.warnings:c:\\Users\\jadamski\\.conda\\envs\\empatia\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
+       "              learning_rate=&#x27;adaptive&#x27;, max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
+       "              learning_rate=&#x27;adaptive&#x27;, max_iter=500)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
+       "              learning_rate='adaptive', max_iter=500)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(x_train, y_train)"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -624,11 +832,16 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
-   "source": []
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(x_test)\n"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -636,11 +849,25 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dokładność: 80.56%\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)\n",
+    "print(\"Dokładność: {:.2f}%\".format(accuracy * 100))\n"
+   ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
diff --git a/data.csv b/data.csv
new file mode 100644
index 0000000..bc3b1ad
--- /dev/null
+++ b/data.csv
@@ -0,0 +1,81 @@
+angry0,box0,disgust0,fear0,happy0,neutral0,sad0,surprise0
+0.19,"[72, 192, 207, 269]",0.0,0.06,0.0,0.58,0.17,0.0
+0.14,"[72, 192, 208, 269]",0.0,0.04,0.0,0.68,0.14,0.0
+0.2,"[71, 191, 208, 271]",0.0,0.02,0.0,0.62,0.15,0.0
+0.24,"[71, 192, 208, 267]",0.0,0.02,0.0,0.59,0.15,0.0
+0.28,"[72, 193, 206, 266]",0.0,0.02,0.0,0.56,0.13,0.0
+0.33,"[69, 189, 209, 265]",0.0,0.04,0.0,0.51,0.11,0.0
+0.3,"[70, 191, 205, 262]",0.0,0.02,0.0,0.56,0.12,0.0
+0.25,"[69, 190, 206, 263]",0.0,0.02,0.0,0.62,0.11,0.0
+0.22,"[69, 190, 206, 262]",0.0,0.02,0.0,0.62,0.14,0.0
+0.17,"[73, 189, 203, 264]",0.0,0.02,0.0,0.67,0.14,0.0
+0.17,"[72, 188, 203, 262]",0.0,0.02,0.0,0.7,0.1,0.0
+0.16,"[72, 187, 202, 263]",0.0,0.02,0.0,0.7,0.11,0.0
+0.12,"[72, 187, 204, 263]",0.0,0.02,0.0,0.78,0.08,0.0
+0.1,"[72, 186, 204, 263]",0.0,0.03,0.0,0.79,0.09,0.0
+0.15,"[70, 185, 206, 264]",0.0,0.03,0.0,0.7,0.12,0.0
+0.18,"[69, 186, 206, 260]",0.0,0.04,0.0,0.64,0.14,0.0
+0.14,"[70, 188, 205, 255]",0.0,0.04,0.0,0.67,0.14,0.0
+0.17,"[71, 189, 204, 254]",0.0,0.03,0.0,0.63,0.17,0.0
+0.36,"[61, 180, 213, 265]",0.0,0.01,0.0,0.54,0.08,0.0
+0.24,"[71, 189, 203, 257]",0.0,0.03,0.0,0.59,0.14,0.0
+0.15,"[68, 188, 204, 258]",0.0,0.03,0.0,0.64,0.17,0.0
+0.24,"[64, 181, 210, 265]",0.0,0.03,0.0,0.49,0.24,0.0
+0.35,"[64, 179, 209, 264]",0.0,0.02,0.0,0.5,0.13,0.0
+0.44,"[69, 182, 205, 255]",0.0,0.02,0.0,0.31,0.23,0.0
+0.41,"[71, 182, 202, 252]",0.0,0.01,0.0,0.27,0.3,0.0
+0.44,"[71, 180, 201, 251]",0.0,0.01,0.0,0.3,0.25,0.0
+0.41,"[65, 177, 207, 258]",0.0,0.02,0.0,0.34,0.23,0.0
+0.48,"[73, 181, 200, 248]",0.0,0.03,0.0,0.2,0.29,0.0
+0.4,"[72, 179, 201, 251]",0.0,0.05,0.0,0.21,0.34,0.0
+0.39,"[61, 172, 208, 257]",0.0,0.03,0.0,0.42,0.15,0.0
+0.4,"[63, 174, 205, 252]",0.0,0.04,0.0,0.37,0.19,0.0
+0.49,"[64, 175, 205, 252]",0.0,0.04,0.0,0.22,0.25,0.0
+0.42,"[65, 174, 204, 251]",0.0,0.03,0.0,0.32,0.21,0.0
+0.34,"[64, 175, 204, 250]",0.0,0.06,0.0,0.33,0.27,0.0
+0.42,"[64, 174, 205, 251]",0.0,0.05,0.0,0.28,0.24,0.0
+0.45,"[64, 174, 205, 251]",0.0,0.05,0.0,0.26,0.23,0.0
+0.48,"[63, 173, 206, 253]",0.0,0.04,0.0,0.27,0.2,0.01
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