salary/salary-training-pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected. Call `.close()` to terminate connection gracefully.\n",
      "\n",
      "Multiple projects found. \n",
      "\n",
      "\t (1) dancip00\n",
      "\t (2) goodonions_id2223_lab1\n",
      "Invalid input, should be an integer from the list of projects.\n",
      "\n",
      "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/217237\n",
      "Connected. Call `.close()` to terminate connection gracefully.\n"
     ]
    }
   ],
   "source": [
    "import hopsworks\n",
    "\n",
    "project = hopsworks.login()\n",
    "fs = project.get_feature_store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "# The feature view is the input set of features for your model. The features can come from different feature groups.\n",
    "# You can select features from different feature groups and join them together to create a feature view\n",
    "salary_fg = fs.get_feature_group(name=\"salary\", version=2)\n",
    "query = salary_fg.select_all()\n",
    "feature_view = fs.get_or_create_feature_view(name=\"salary\",\n",
    "                                             query=query,\n",
    "                                             labels=[\"salary_bins\"],\n",
    "                                             version=2)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished: Reading data from Hopsworks, using ArrowFlight (1.28s) \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "VersionWarning: Incremented version to `7`.\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = feature_view.train_test_split(test_size=0.2)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "'''\n",
    "y_train = X_train[\"salary_bins\"]\n",
    "y_test = X_test[\"salary_bins\"]\n",
    "'''\n",
    "X_train = X_train.drop(columns=[ \"salary\"])\n",
    "X_test = X_test.drop(columns=[\"salary\"])\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "   work_year  experience_level  company_size    eur    gbp   usd  engineer  \\\n0       2023                 2             1  False  False  True         1   \n1       2022                 2             1  False  False  True         0   \n2       2023                 2             1  False  False  True         1   \n4       2023                 1             1  False  False  True         1   \n6       2023                 1             1  False  False  True         1   \n\n   scientist  research  analyst  ...  data_science_manager  data_scientist  \\\n0          0         0        0  ...                 False           False   \n1          0         0        1  ...                 False           False   \n2          0         0        0  ...                 False           False   \n4          0         0        0  ...                 False           False   \n6          0         0        0  ...                 False           False   \n\n   ml_engineer  machine_learning_engineer  machine_learning_scientist  \\\n0        False                      False                       False   \n1        False                      False                       False   \n2        False                      False                       False   \n4        False                      False                       False   \n6        False                      False                       False   \n\n   research_analyst  research_engineer  research_scientist           gdp  \\\n0             False              False               False  2.543970e+13   \n1             False              False               False  2.543970e+13   \n2             False              False               False  2.543970e+13   \n4             False              False               False  2.543970e+13   \n6             False              False               False  2.543970e+13   \n\n          cpi  \n0  134.211206  \n1  134.211206  \n2  134.211206  \n4  134.211206  \n6  134.211206  \n\n[5 rows x 30 columns]",
      "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>work_year</th>\n      <th>experience_level</th>\n      <th>company_size</th>\n      <th>eur</th>\n      <th>gbp</th>\n      <th>usd</th>\n      <th>engineer</th>\n      <th>scientist</th>\n      <th>research</th>\n      <th>analyst</th>\n      <th>...</th>\n      <th>data_science_manager</th>\n      <th>data_scientist</th>\n      <th>ml_engineer</th>\n      <th>machine_learning_engineer</th>\n      <th>machine_learning_scientist</th>\n      <th>research_analyst</th>\n      <th>research_engineer</th>\n      <th>research_scientist</th>\n      <th>gdp</th>\n      <th>cpi</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023</td>\n      <td>2</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>2.543970e+13</td>\n      <td>134.211206</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2022</td>\n      <td>2</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>2.543970e+13</td>\n      <td>134.211206</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023</td>\n      <td>2</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>2.543970e+13</td>\n      <td>134.211206</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023</td>\n      <td>1</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>2.543970e+13</td>\n      <td>134.211206</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023</td>\n      <td>1</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>2.543970e+13</td>\n      <td>134.211206</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 30 columns</p>\n</div>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "#remove is not a number values in the dataset\n",
    "X_train['salary_bins']= y_train\n",
    "X_train = X_train.dropna()\n",
    "y_train = X_train[\"salary_bins\"]\n",
    "X_train = X_train.drop(columns=[\"salary_bins\"])\n",
    "\n",
    "X_test['salary_bins']= y_test\n",
    "X_test = X_test.dropna()\n",
    "y_test = X_test[\"salary_bins\"]\n",
    "X_test = X_test.drop(columns=[\"salary_bins\"])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=2, random_state=42),\n             n_jobs=4, param_grid=[{'max_leaf_nodes': range(20, 30)}],\n             return_train_score=True, scoring='accuracy')",
      "text/html": "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=2, random_state=42),\n             n_jobs=4, param_grid=[{&#x27;max_leaf_nodes&#x27;: range(20, 30)}],\n             return_train_score=True, scoring=&#x27;accuracy&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=2, random_state=42),\n             n_jobs=4, param_grid=[{&#x27;max_leaf_nodes&#x27;: range(20, 30)}],\n             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=2, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=2, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest and Grid Search\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "param_grid = [\n",
    "    {'max_leaf_nodes': range(20, 30, 1)},\n",
    "]\n",
    "\n",
    "rnd_clf = RandomForestClassifier(random_state=42, bootstrap=True, n_jobs=2)\n",
    "grid_search = GridSearchCV(rnd_clf, param_grid, cv=5, scoring='accuracy', return_train_score=True, n_jobs=4)\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "0.5788876276958003"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = grid_search.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "{'max_leaf_nodes': 28}"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "rnd_clf = RandomForestClassifier(random_state=42, max_leaf_nodes=25, bootstrap=True, n_jobs=2)\n",
    "rnd_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rnd_clf.predict(X_test)\n",
    "ac = accuracy_score(y_test, y_pred)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report\n",
    "import pandas as pd\n",
    "\n",
    "metrics = classification_report(y_test, y_pred, output_dict=True)\n",
    "\n",
    "results = confusion_matrix(y_test, y_pred)\n",
    "df_cm = pd.DataFrame(results, [\"Low\", \"Medium\", \"High\"], columns=[\"Low\", \"Medium\", \"High\"])\n",
    "cm = sns.heatmap(df_cm, annot=True, cmap=\"Blues\")\n",
    "fig = cm.get_figure()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Model upload to Hopsworks"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected. Call `.close()` to terminate connection gracefully.\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/6 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "8896ec997c294fa587a1924a997bf452"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Uploading: 0.000%|          | 0/17725 elapsed<00:00 remaining<?",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "fa316264fa084d67b992b86954d69528"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Uploading: 0.000%|          | 0/474321 elapsed<00:00 remaining<?",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "23ac4986b6264fffb18c6cf668f7133e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Uploading: 0.000%|          | 0/2409 elapsed<00:00 remaining<?",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e9260f9ad5c240d2bb7402430a2b1c44"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model created, explore it at https://c.app.hopsworks.ai:443/p/217237/models/salary_model/5\n"
     ]
    },
    {
     "data": {
      "text/plain": "Model(name: 'salary_model', version: 5)"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hsml.schema import Schema\n",
    "from hsml.model_schema import ModelSchema\n",
    "import joblib\n",
    "import os\n",
    "\n",
    "mr = project.get_model_registry()\n",
    "\n",
    "model_dir = \"model_salary\"\n",
    "if os.path.isdir(model_dir) == False:\n",
    "    os.mkdir(model_dir)\n",
    "\n",
    "joblib.dump(rnd_clf, os.path.join(model_dir, \"model.pkl\"))\n",
    "fig.savefig(os.path.join(model_dir, \"confusion_matrix.png\"))\n",
    "\n",
    "input_schema = Schema(X_train)\n",
    "output_schema = Schema(y_train)\n",
    "model_schema = ModelSchema(input_schema, output_schema)\n",
    "\n",
    "salary_model = mr.python.create_model(\n",
    "    name=\"salary_model\",\n",
    "    metrics={\"accuracy\": ac},\n",
    "    model_schema=model_schema,\n",
    "    description=\"Salary Predictor\"\n",
    ")\n",
    "\n",
    "salary_model.save(model_dir)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}