Fix

Jenkinsfile

@@ -0,0 +1,23 @@
+pipeline {
+    agent any
+
+    stages {
+        stage('Build') {
+            steps {
+                echo 'Building..'
+                // Run black to format all Python files
+                sh 'find . -name "*.py" -exec black {} +'
+            }
+        }
+        stage('Test') {
+            steps {
+                echo 'Testing..'
+            }
+        }
+        stage('Deploy') {
+            steps {
+                echo 'Deploying....'
+            }
+        }
+    }
+}

Mieszkania.ipynb

@@ -1,212 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Ładowanie danych:"
-      ],
-      "metadata": {
-        "id": "coWdAJZAPC1C"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bozs99nnO2jv",
-        "outputId": "4119ebc8-eccf-4574-866c-2502176e0fbd"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "fatal: destination path 'mieszkania5' already exists and is not an empty directory.\n"
-          ]
-        }
-      ],
-      "source": [
-        "!git clone git://gonito.net/mieszkania5"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Importy:"
-      ],
-      "metadata": {
-        "id": "OFaZTYDGQqLQ"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import csv\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "\n",
-        "data = pd.read_table(\"mieszkania5/train/train.tsv\", delimiter='\\t', header=None)\n",
-        "data.rename(columns={0: 'cena', 1: 'stan', 2: 'czynsz', 3: 'x3', 4: 'cenazam', 5: 'link', 6: 'pietro', 7: 'x7', 8: 'metraz', 9: 'rynek', 10: 'liczba pokoi', 11: 'budynek', 12: 'x12', 13: 'x13', 14: 'x14', 15: 'x15', 16: 'x16', 17: 'x17', 18: 'x18', 19: 'x19', 20: 'x20', 21: 'x21', 22: 'x22', 23: 'x23', 24: 'x24', 25: 'x25'}, inplace=True)\n",
-        "\n",
-        "data.drop('x3', inplace=True, axis=1)\n",
-        "data.drop('cenazam', inplace=True, axis=1)\n",
-        "data.drop('link', inplace=True, axis=1)\n",
-        "data.drop('pietro', inplace=True, axis=1)\n",
-        "data.drop('budynek', inplace=True, axis=1)\n",
-        "data.drop('x7', inplace=True, axis=1)\n",
-        "data.drop('x12', inplace=True, axis=1)\n",
-        "data.drop('x13', inplace=True, axis=1)\n",
-        "data.drop('x14', inplace=True, axis=1)\n",
-        "data.drop('x15', inplace=True, axis=1)\n",
-        "data.drop('x16', inplace=True, axis=1)\n",
-        "data.drop('x17', inplace=True, axis=1)\n",
-        "data.drop('x18', inplace=True, axis=1)\n",
-        "data.drop('x19', inplace=True, axis=1)\n",
-        "data.drop('x20', inplace=True, axis=1)\n",
-        "data.drop('x21', inplace=True, axis=1)\n",
-        "data.drop('x22', inplace=True, axis=1)\n",
-        "data.drop('x23', inplace=True, axis=1)\n",
-        "data.drop('x24', inplace=True, axis=1)\n",
-        "data.drop('x25', inplace=True, axis=1)\n",
-        "\n",
-        "data['czynsz'] = data['czynsz'].str.extract('(\\d+)')\n",
-        "data['stan'] = data['stan'].map({'do zamieszkania': 2, 'do remontu': 1, 'do wykończenia': 2})\n",
-        "data['rynek'] = data['rynek'].map({'wtórny': 0, 'pierwotny': 1})\n",
-        "\n",
-        "data.dropna(inplace=True)"
-      ],
-      "metadata": {
-        "id": "K-TUB0pAPCp2"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [],
-      "metadata": {
-        "id": "57zFDlw7PDDb"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "cena = data['cena']\n",
-        "parametry = data[['stan', 'czynsz', 'liczba pokoi', 'metraz', 'rynek']]"
-      ],
-      "metadata": {
-        "id": "___F5VBeco6H"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.linear_model import LinearRegression"
-      ],
-      "metadata": {
-        "id": "H1shMEsxTccr"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def train_model(cena, parametry):\n",
-        "  model = LinearRegression()\n",
-        "  model.fit(X=parametry, y=cena)\n",
-        "  return model"
-      ],
-      "metadata": {
-        "id": "vT9sCZ2XTjKy"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model = train_model(cena, parametry)"
-      ],
-      "metadata": {
-        "id": "-DZ-HNMtUBmr"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def predict(stan, czynsz, liczba_pokoi, metraz, rynek):\n",
-        "  return model.predict(np.array([[stan, czynsz, liczba_pokoi, metraz, rynek]])).item()"
-      ],
-      "metadata": {
-        "id": "oK_ZW9N9Wg2u"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "predict(1, 200, 2, 40.0, 0)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bLmRBRBMgFTg",
-        "outputId": "f94f3691-9a2a-4035-b3ad-dde097631e85"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "217119.72285625804"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 60
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "K7eEdZFzgI3n"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}

dev-0/expected.tsv

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pipeline.py

@@ -0,0 +1,26 @@
+import csv
+import model_training as mt
+import pandas as pd
+import price_evaluation as pe
+
+def eval(filename_in, filename_out):
+    data_eval = pd.read_table(filename_in, delimiter='\t', header=None)
+    data_eval.rename(columns={0: 'stan', 1: 'czynsz', 2: 'x3', 3: 'cenazam', 4: 'link', 5: 'pietro', 6: 'x7', 7: 'metraz', 8: 'rynek', 9: 'liczba pokoi', 10: 'budynek', 11: 'x12', 12: 'x13', 13: 'x14', 14: 'x15', 15: 'x16', 16: 'x17', 17: 'x18', 18: 'x19', 19: 'x20', 20: 'x21', 21: 'x22', 22: 'x23', 23: 'x24', 24: 'x25'}, inplace=True)
+
+    data_eval.drop(['x3','cenazam','link','pietro','budynek','x7','x12','x13','x14','x15','x16','x17','x18','x19','x20','x21','x22','x23','x24','x25'], inplace=True, axis=1)
+    data_eval['czynsz'] = data_eval['czynsz'].str.extract('(\d+)')
+    data_eval['stan'] = data_eval['stan'].map({'do zamieszkania': 2, 'do remontu': 1, 'do wykończenia': 2})
+    data_eval['rynek'] = data_eval['rynek'].map({'wtórny': 0, 'pierwotny': 1})
+    data_eval['stan'] = data_eval['stan'].fillna(0)
+    data_eval['czynsz'] = data_eval['czynsz'].fillna(200)
+    data_eval['liczba pokoi'] = data_eval['liczba pokoi'].fillna(2)
+    data_eval['metraz'] = data_eval['metraz'].fillna(40.0)
+    data_eval['rynek'] = data_eval['rynek'].fillna(0)
+    data_eval.dropna(inplace=True)
+    data_eval = data_eval[['stan', 'czynsz', 'liczba pokoi', 'metraz', 'rynek']]
+
+    out = mt.model.predict(data_eval)
+    pd.DataFrame(out).to_csv(filename_out, sep='\t', index=False, header=False)
+
+eval('dev-0/in.tsv', 'dev-0/out.tsv')
+eval('test-A/in.tsv', 'test-A/out.tsv')

test-A/out.tsv

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