04_design_testing_my_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A/B Testing Course"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lesson 4. Desing Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Homework"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 580,
     "status": "ok",
     "timestamp": 1674542192205,
     "user": {
      "displayName": "Nikolay Nazarov",
      "userId": "18018785782997408667"
     },
     "user_tz": -420
    },
    "id": "agQEC38nE-72",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm, ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1674542192206,
     "user": {
      "displayName": "Nikolay Nazarov",
      "userId": "18018785782997408667"
     },
     "user_tz": -420
    },
    "id": "xxFeCfFuFKng",
    "tags": []
   },
   "outputs": [],
   "source": [
    "URL_BASE = ''\n",
    "\n",
    "def read_database(file_name):\n",
    "    return pd.read_csv(os.path.join(URL_BASE, file_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 6399,
     "status": "ok",
     "timestamp": 1674542198600,
     "user": {
      "displayName": "Nikolay Nazarov",
      "userId": "18018785782997408667"
     },
     "user_tz": -420
    },
    "id": "nQGxlqByE-73",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_sales = read_database('2022-04-01T12_df_sales.csv')\n",
    "df_sales['date'] = pd.to_datetime(df_sales['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nNRTGF6aBHrZ"
   },
   "source": [
    "df_sales - information about purchases, one row represents one order:\n",
    "- sale_id - purchase identifier;\n",
    "- date - date of purchase;\n",
    "- count_pizza - number of pizzas in the order;\n",
    "- count_drink - number of drinks in the order;\n",
    "- price - order price;\n",
    "- user_id - user identifier.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Checking Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "executionInfo": {
     "elapsed": 53,
     "status": "ok",
     "timestamp": 1674542198602,
     "user": {
      "displayName": "Nikolay Nazarov",
      "userId": "18018785782997408667"
     },
     "user_tz": -420
    },
    "id": "0dZkzMMTE-73",
    "outputId": "44f7566a-b32d-4479-b963-7c5977351149",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale_id</th>\n",
       "      <th>date</th>\n",
       "      <th>count_pizza</th>\n",
       "      <th>count_drink</th>\n",
       "      <th>price</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>2022-02-04 10:00:24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>720</td>\n",
       "      <td>1c1543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000002</td>\n",
       "      <td>2022-02-04 10:02:28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>930</td>\n",
       "      <td>a9a6e8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000003</td>\n",
       "      <td>2022-02-04 10:02:35</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1980</td>\n",
       "      <td>23420a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000004</td>\n",
       "      <td>2022-02-04 10:03:06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>750</td>\n",
       "      <td>3e8ed5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000005</td>\n",
       "      <td>2022-02-04 10:03:23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>870</td>\n",
       "      <td>cbc468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sale_id                date  count_pizza  count_drink  price user_id\n",
       "0  1000001 2022-02-04 10:00:24            1            0    720  1c1543\n",
       "1  1000002 2022-02-04 10:02:28            1            1    930  a9a6e8\n",
       "2  1000003 2022-02-04 10:02:35            3            1   1980  23420a\n",
       "3  1000004 2022-02-04 10:03:06            1            1    750  3e8ed5\n",
       "4  1000005 2022-02-04 10:03:23            1            1    870  cbc468"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 203847 entries, 0 to 203846\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   sale_id      203847 non-null  int64         \n",
      " 1   date         203847 non-null  datetime64[ns]\n",
      " 2   count_pizza  203847 non-null  int64         \n",
      " 3   count_drink  203847 non-null  int64         \n",
      " 4   price        203847 non-null  int64         \n",
      " 5   user_id      203847 non-null  object        \n",
      "dtypes: datetime64[ns](1), int64(4), object(1)\n",
      "memory usage: 9.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_sales.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose all users who made purchases before March 28th are included in our A/B test.\n",
    "\n",
    "The target metric is the average revenue per customer during the experiment. We will calculate the target metric for the week from March 21st to March 28th. The significance level is 0.05. The criterion is Student's t-test. The group size is 1000. The expected effect is an increase in average revenue by 10%.\n",
    "\n",
    "We need to estimate the probabilities of type II errors for three ways of adding the effect:\n",
    "1. Adding a constant to all values;\n",
    "1. Multiplying all values by a constant;\n",
    "1. Adding a constant to 2.5% of values.\n",
    "\n",
    "Enter the numbers of the methods of adding the effect in order of increasing estimation of the probability of type II error. For example, if the estimation of the probability of error is the lowest when adding a constant to all values, and the highest when adding a constant to 2.5% of values, the answer would be: 123."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# filtering data by date\n",
    "sales_filt = df_sales[(df_sales['date'] >= '2022-03-21') & \\\n",
    "                      (df_sales['date'] < '2022-03-28')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# function to plot p-value graphs\n",
    "def plot_pvalue_ecdf(pvalues, title=None):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "    if title:\n",
    "        plt.suptitle(title)\n",
    "\n",
    "    sns.histplot(pvalues, ax=ax1, bins=20, stat='density')\n",
    "    ax1.plot([0,1],[1,1], 'k--')\n",
    "    ax1.set(xlabel='p-value', ylabel='Density')\n",
    "\n",
    "    sns.ecdfplot(pvalues, ax=ax2)\n",
    "    ax2.plot([0,1],[0,1], 'k--')\n",
    "    ax2.axvline(x=0.05, color='grey')\n",
    "    \n",
    "    # Define the x value you want to get the y value for\n",
    "    x = 0.05\n",
    "    line = ax2.get_lines()[0]\n",
    "    f = interp1d(line.get_xdata(), line.get_ydata())\n",
    "    y = f(x)\n",
    "    print(f\"The Beta value for alpha={x} is {y:.2f}\")\n",
    "    \n",
    "    ax2.set(xlabel='p-value', ylabel='Probability')\n",
    "    ax2.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding a constant to all values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Beta value for alpha=0.05 is 0.93\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "alpha = 0.05\n",
    "effect = 0.1\n",
    "\n",
    "\n",
    "p_values = []\n",
    "grouped_sales = sales_filt.groupby('user_id', as_index=False).agg({'price': 'sum'}).reset_index()\n",
    "mean = grouped_sales['price'].mean()\n",
    "std = grouped_sales['price'].std()\n",
    "users = sales_filt['user_id'].unique().tolist()\n",
    "for _ in range(10000):\n",
    "    np.random.shuffle(users)\n",
    "    group_a = random.sample(users, sample_size)\n",
    "    group_b = random.sample(users, sample_size)\n",
    "    sales_a = grouped_sales[grouped_sales['user_id'].isin(group_a)]['price']\n",
    "    sales_b = grouped_sales[grouped_sales['user_id'].isin(group_b)]['price']\n",
    "    sales_b = sales_b + mean * effect\n",
    "    _, p_val = ttest_ind(sales_a, sales_b)\n",
    "    p_values.append(p_val)\n",
    "plot_pvalue_ecdf(p_values, 'p-value ECDF. Independent data with effect = mde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiplying all values by a constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Beta value for alpha=0.05 is 0.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = []\n",
    "grouped_sales = sales_filt.groupby('user_id', as_index=False).agg({'price': 'sum'}).reset_index()\n",
    "users = sales_filt['user_id'].unique().tolist()\n",
    "for _ in range(10000):\n",
    "    np.random.shuffle(users)\n",
    "    group_a = random.sample(users, sample_size)\n",
    "    group_b = random.sample(users, sample_size)\n",
    "    sales_a = grouped_sales[grouped_sales['user_id'].isin(group_a)]['price']\n",
    "    sales_b = grouped_sales[grouped_sales['user_id'].isin(group_b)]['price']\n",
    "    sales_b = sales_b * (1 + effect)\n",
    "    _, p_val = ttest_ind(sales_a, sales_b)\n",
    "    p_values.append(p_val)\n",
    "plot_pvalue_ecdf(p_values, 'p-value ECDF. Independent data with effect = mde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding a constant to 2.5% of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Beta value for alpha=0.05 is 0.86\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_values = []\n",
    "grouped_sales = sales_filt.groupby('user_id', as_index=False).agg({'price': 'sum'}).reset_index()\n",
    "users = sales_filt['user_id'].unique().tolist()\n",
    "for _ in range(10000):\n",
    "    np.random.shuffle(users)\n",
    "    group_a = random.sample(users, sample_size)\n",
    "    group_b = random.sample(users, sample_size)\n",
    "    sales_a = grouped_sales[grouped_sales['user_id'].isin(group_a)]['price']\n",
    "    sales_b = grouped_sales[grouped_sales['user_id'].isin(group_b)]['price']\n",
    "    indexes = np.random.choice(np.arange(sample_size), int(sample_size * 0.025), False)\n",
    "    add_value = effect * mean * sample_size / len(indexes)\n",
    "    sales_b1 = sales_b[:len(indexes)] + add_value\n",
    "    sales_b2 = sales_b[len(indexes):]\n",
    "    sales_b = sales_b1.append(sales_b2)\n",
    "    _, p_val = ttest_ind(sales_a, sales_b)\n",
    "    p_values.append(p_val)\n",
    "plot_pvalue_ecdf(p_values, 'p-value ECDF. Independent data with effect = mde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task 2. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Today we need to implement the code to estimate the probabilities of type I and type II errors.   \n",
    "Write the method `_estimate_errors` of the class `ExperimentsService`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simple test passed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pydantic import BaseModel\n",
    "from scipy import stats\n",
    "\n",
    "\n",
    "class Design(BaseModel):\n",
    "    \"\"\"Data class with a description of experiment parameters.\n",
    "    \n",
    "    statistical_test - type of statistical test. ['ttest']\n",
    "    effect - effect size in percentage\n",
    "    alpha - significance level\n",
    "    beta - allowable probability of type II error\n",
    "    sample_size - group size\n",
    "    \"\"\"\n",
    "    statistical_test: str = 'ttest'\n",
    "    effect: float\n",
    "    alpha: float = 0.05\n",
    "    beta: float = 0.1\n",
    "    sample_size: int\n",
    "\n",
    "\n",
    "class ExperimentsService:\n",
    "\n",
    "    def get_pvalue(self, metrics_a_group, metrics_b_group, design):\n",
    "        \"\"\"Applies a statistical test and returns p-value.\n",
    "        \n",
    "        :param metrics_a_group (np.array): an array of metric values for group A\n",
    "        :param metrics_b_group (np.array): an array of metric values for group B\n",
    "        :param design (Design): an object containing experiment parameters data\n",
    "        :return (float): p-value\n",
    "        \"\"\"\n",
    "        if design.statistical_test == 'ttest':\n",
    "            _, pvalue = stats.ttest_ind(metrics_a_group, metrics_b_group)\n",
    "            return pvalue\n",
    "        else:\n",
    "            raise ValueError('Wrong design.statistical_test')\n",
    "\n",
    "    def _create_group_generator(self, metrics, sample_size, n_iter):\n",
    "        \"\"\"Random groups generator.\n",
    "        \n",
    "        :param metrics (pd.DataFame): a table with metrics, columns=['user_id', 'metric'].\n",
    "        :param sample_size (int): group size (number of users in the group).\n",
    "        :param n_iter (int): number of iterations of random group generation.\n",
    "        :return (np.array, np.array): two arrays with metric values in groups.\n",
    "        \"\"\"\n",
    "        user_ids = metrics['user_id'].unique()\n",
    "        for _ in range(n_iter):\n",
    "            a_user_ids, b_user_ids = np.random.choice(user_ids, (2, sample_size), False)\n",
    "            a_metric_values = metrics.loc[metrics['user_id'].isin(a_user_ids), 'metric'].values\n",
    "            b_metric_values = metrics.loc[metrics['user_id'].isin(b_user_ids), 'metric'].values\n",
    "            yield a_metric_values, b_metric_values\n",
    "\n",
    "    def _estimate_errors(self, group_generator, design, effect_add_type):\n",
    "        \"\"\"Estimating the probabilities of Type I and Type II errors.\n",
    "\n",
    "        :param group_generator: generator of metric values for two groups.\n",
    "        :param design (Design): object with experiment parameters description.\n",
    "        :param effect_add_type (str): way of adding effect for group B.\n",
    "            - 'all_const' - increase all values in group B by a constant (b_metric_values.mean() * effect / 100).\n",
    "            - 'all_percent' - increase all values in group B by a factor of (1 + effect / 100).\n",
    "        :return pvalues_aa (list[float]), pvalues_ab (list[float]), first_type_error (float), second_type_error (float):\n",
    "            - pvalues_aa, pvalues_ab - lists of p-value values\n",
    "            - first_type_error, second_type_error - estimates of the probabilities of Type I and Type II errors.\n",
    "        \"\"\"\n",
    "        # YOUR_CODE_HERE\n",
    "        pvalues_aa = []\n",
    "        pvalues_ab = []\n",
    "        eff = design.effect\n",
    "        alpha = design.alpha\n",
    "        \n",
    "        for value in group_generator:\n",
    "            group_a, group_b = value\n",
    "            \n",
    "            pvalues_aa.append(self.get_pvalue(group_a, group_b, design))\n",
    "\n",
    "            if effect_add_type == 'all_const':\n",
    "                effect = group_b.mean() * eff / 100\n",
    "                group_b = group_b + effect\n",
    "            elif effect_add_type == 'all_percent':\n",
    "                effect = 1 + eff / 100\n",
    "                group_b = group_b * effect\n",
    "\n",
    "            pvalues_ab.append(self.get_pvalue(group_a, group_b, design))\n",
    "        \n",
    "        first = [x for x in pvalues_aa if x < alpha]\n",
    "        second = [x for x in pvalues_ab if x > alpha]\n",
    "        \n",
    "        first_type_error = len(first) / len(pvalues_aa)\n",
    "        second_type_error = len(second) / len(pvalues_ab)\n",
    "        \n",
    "        return pvalues_aa, pvalues_ab, first_type_error, second_type_error\n",
    "\n",
    "    def estimate_errors(self, metrics, design, effect_add_type, n_iter):\n",
    "        \"\"\"Estimating the probabilities of Type I and Type II errors.\n",
    "\n",
    "            :param metrics (pd.DataFrame): a table with metrics, columns=['user_id', 'metric'].\n",
    "            :param design (Design): an object with data describing the experiment parameters.\n",
    "            :param effect_add_type (str): the way to add the effect for group B.\n",
    "                - 'all_const' - increase all values in group B by a constant (b_metric_values.mean() * effect / 100).\n",
    "                - 'all_percent' - increase all values in group B by (1 + effect / 100) times.\n",
    "            :param n_iter (int): the number of iterations of generating random groups.\n",
    "            :return pvalues_aa (list[float]), pvalues_ab (list[float]), first_type_error (float), second_type_error (float):\n",
    "                - pvalues_aa, pvalues_ab - lists with pvalue values\n",
    "                - first_type_error, second_type_error - estimates of the probabilities of type I and II errors.\n",
    "        \"\"\"\n",
    "        group_generator = self._create_group_generator(metrics, design.sample_size, n_iter)\n",
    "        return self._estimate_errors(group_generator, design, effect_add_type)\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    _a = np.array([1., 2, 3, 4, 5])\n",
    "    _b = np.array([1., 2, 3, 4, 10])\n",
    "    group_generator = ([a, b] for a, b in ((_a, _b),))\n",
    "    design = Design(effect=50., sample_size=5)\n",
    "    effect_add_type = 'all_percent'\n",
    "\n",
    "    ideal_pvalues_aa = [0.579584]\n",
    "    ideal_pvalues_ab = [0.260024]\n",
    "    ideal_first_type_error = 0.\n",
    "    ideal_second_type_error = 1.\n",
    "\n",
    "    experiments_service = ExperimentsService()\n",
    "    pvalues_aa, pvalues_ab, first_type_error, second_type_error = experiments_service._estimate_errors(\n",
    "        group_generator, design, effect_add_type\n",
    "    )\n",
    "    np.testing.assert_almost_equal(ideal_pvalues_aa, pvalues_aa, decimal=4)\n",
    "    np.testing.assert_almost_equal(ideal_pvalues_ab, pvalues_ab, decimal=4)\n",
    "    assert ideal_first_type_error == first_type_error\n",
    "    assert ideal_second_type_error == second_type_error\n",
    "    print('simple test passed')"
   ]
  }
 ],
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