load_all_compute_plans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a83c9cea-845a-40b1-ae24-fb689732b965",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4595effe-d873-4acf-8293-847934238da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Excel file (the files need to be downloaded next to this script)\n",
    "file = 'all_compute_plans_melloddy.csv'\n",
    "df = pd.read_csv(file, delimiter=',', low_memory=False)\n",
    "df = df.dropna(subset=['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49978c36-63b1-4cc5-82d0-34e81eb36387",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_df(df, phase=[], dataset=[], model_type=[], partner=[]):\n",
    "    \"\"\"\n",
    "    Filters the dataframe of compute plans, based on the the phase, dataset, model_type and partner\n",
    "    AND filtering is performed between parameters\n",
    "    OR filtering is performed within parameters\n",
    "    \n",
    "    Args\n",
    "        df (pandas.DataFrame): data to filter\n",
    "        phase, dataset, model_type, partner (lists): values to filter on\n",
    "    \n",
    "    Returns\n",
    "        df_filtered (pandas.DataFrame): filtered data\n",
    "    \"\"\"\n",
    "    \n",
    "    df_filtered = df.copy()\n",
    "    \n",
    "    # words to keep in the column \"name\"\n",
    "    filters_name = [phase, dataset, model_type]\n",
    "    for words in filters_name: \n",
    "        df_filtered = df_filtered[df_filtered.name.str.contains('|'.join(words))]\n",
    "        \n",
    "    # words to keep in the column \"worker\"\n",
    "    filters_worker = [partner]\n",
    "    for words in filters_worker: \n",
    "        df_filtered = df_filtered[df_filtered.worker.str.contains('|'.join(words))]\n",
    "    \n",
    "    return df_filtered"
   ]
  },
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   "id": "348ba8e6-f41a-48a7-bc63-86cef37028df",
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       "      <td>cp10-MP-classification-PH1</td>\n",
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       "                                        key                        name   tag  \\\n",
       "32192  c5d98535-0a7d-40b1-99c8-462afd6edc2c  cp10-MP-classification-PH1  cp10   \n",
       "32193  c5d98535-0a7d-40b1-99c8-462afd6edc2c  cp10-MP-classification-PH1  cp10   \n",
       "32194  c5d98535-0a7d-40b1-99c8-462afd6edc2c  cp10-MP-classification-PH1  cp10   \n",
       "32195  c5d98535-0a7d-40b1-99c8-462afd6edc2c  cp10-MP-classification-PH1  cp10   \n",
       "32196  c5d98535-0a7d-40b1-99c8-462afd6edc2c  cp10-MP-classification-PH1  cp10   \n",
       "\n",
       "       status                     start_date                       end_date  \\\n",
       "32192  Failed  2022-01-14 12:48:55.170035+01  2022-01-14 13:24:33.032369+01   \n",
       "32193  Failed  2022-01-14 12:48:55.170035+01  2022-01-14 13:24:33.032369+01   \n",
       "32194  Failed  2022-01-14 12:48:55.170035+01  2022-01-14 13:24:33.032369+01   \n",
       "32195  Failed  2022-01-14 12:48:55.170035+01  2022-01-14 13:24:33.032369+01   \n",
       "32196  Failed  2022-01-14 12:48:55.170035+01  2022-01-14 13:24:33.032369+01   \n",
       "\n",
       "       epochs hidden_sizes  last_hidden_sizes  lr  ...  test_task_rank  \\\n",
       "32192    30.0         8000                NaN NaN  ...             0.0   \n",
       "32193    30.0         8000                NaN NaN  ...             0.0   \n",
       "32194    30.0         8000                NaN NaN  ...             0.0   \n",
       "32195    30.0         8000                NaN NaN  ...             0.0   \n",
       "32196    30.0         8000                NaN NaN  ...            98.0   \n",
       "\n",
       "       test_task_epoch  n_minibatches  \\\n",
       "32192              0.0            0.0   \n",
       "32193              0.0            0.0   \n",
       "32194              0.0            0.0   \n",
       "32195              0.0            0.0   \n",
       "32196              1.0           50.0   \n",
       "\n",
       "                                             metric_name  performance  \\\n",
       "32192  Classification on Bioactivity for metric_cls_b...     1.147315   \n",
       "32193  Classification on Bioactivity for metric_cls_a...     0.114595   \n",
       "32194  Classification on Bioactivity for metric_cls_r...     0.499028   \n",
       "32195  Classification on Bioactivity for metric_cls_a...     0.360151   \n",
       "32196  Classification on Bioactivity for metric_cls_r...          NaN   \n",
       "\n",
       "        average  pharma_average large5_average small5_average        platform  \n",
       "32192  1.096219             NaN            NaN            NaN  platform1_1400  \n",
       "32193  0.121481             NaN            NaN            NaN  platform1_1400  \n",
       "32194  0.501499             NaN            NaN            NaN  platform1_1400  \n",
       "32195  0.411814             NaN            NaN            NaN  platform1_1400  \n",
       "32196       NaN             NaN            NaN            NaN  platform1_1400  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phase = ['PH1'] # 'PH1', 'PH2', 'PH3'\n",
    "dataset = ['classification'] # 'classification', 'regression', 'clsaux', 'hybrid'\n",
    "model_type = ['MP'] # 'SP', 'MP'\n",
    "partner = ['pharma1'] # 'pharma1', 'pharma2', 'pharma3', 'pharma4','pharma5', 'pharma6', 'pharma7', 'pharma8', 'pharma9', 'pharma10'\n",
    "\n",
    "df_filtered = filter_df(df, phase, dataset, model_type, partner)\n",
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53af43a6-f56b-4151-b909-ed2653c233ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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