{
"cells": [
{
"cell_type": "markdown",
"id": "5c5c3f1c",
"metadata": {},
"source": [
"# Imputation using random draws from shifted normal distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e38874f3",
"metadata": {
"lines_to_next_cell": 2,
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"import logging\n",
"\n",
"import pandas as pd\n",
"from IPython.display import display\n",
"\n",
"import pimmslearn\n",
"import pimmslearn.imputation\n",
"import pimmslearn.model\n",
"import pimmslearn.models as models\n",
"import pimmslearn.nb\n",
"from pimmslearn.io import datasplits\n",
"\n",
"logger = pimmslearn.logging.setup_logger(logging.getLogger('pimmslearn'))\n",
"logger.info(\"Median Imputation\")\n",
"\n",
"figures = {} # collection of ax or figures"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca2c3fb3",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"# catch passed parameters\n",
"args = None\n",
"args = dict(globals()).keys()"
]
},
{
"cell_type": "markdown",
"id": "23955adb",
"metadata": {},
"source": [
"Papermill script parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33375547",
"metadata": {
"lines_to_next_cell": 2,
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"# files and folders\n",
"# Datasplit folder with data for experiment\n",
"folder_experiment: str = 'runs/example'\n",
"file_format: str = 'csv' # file format of create splits, default pickle (pkl)\n",
"# Machine parsed metadata from rawfile workflow\n",
"fn_rawfile_metadata: str = 'data/dev_datasets/HeLa_6070/files_selected_metadata_N50.csv'\n",
"# model\n",
"sample_idx_position: int = 0 # position of index which is sample ID\n",
"# model key (lower cased version will be used for file names)\n",
"axis: int = 1 # impute per row/sample (1) or per column/feat (0).\n",
"completeness = 0.6 # fractio of non missing values for row/sample (axis=0) or column/feat (axis=1)\n",
"model_key: str = 'RSN'\n",
"model: str = 'RSN' # model name\n",
"save_pred_real_na: bool = True # Save all predictions for real na\n",
"# metadata -> defaults for metadata extracted from machine data\n",
"meta_date_col: str = None # date column in meta data\n",
"meta_cat_col: str = None # category column in meta data"
]
},
{
"cell_type": "markdown",
"id": "924a4f0d",
"metadata": {
"lines_to_next_cell": 2
},
"source": [
"Some argument transformations"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36f708a2",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"args = pimmslearn.nb.get_params(args, globals=globals())\n",
"args"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4bb6bf2",
"metadata": {
"lines_to_next_cell": 2,
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"args = pimmslearn.nb.args_from_dict(args)\n",
"args"
]
},
{
"cell_type": "markdown",
"id": "c467efb4",
"metadata": {},
"source": [
"Some naming conventions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3ded735",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"TEMPLATE_MODEL_PARAMS = 'model_params_{}.json'"
]
},
{
"cell_type": "markdown",
"id": "dea94507",
"metadata": {},
"source": [
"## Load data in long format"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92d787e1",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"data = datasplits.DataSplits.from_folder(\n",
" args.data, file_format=args.file_format)"
]
},
{
"cell_type": "markdown",
"id": "191bcf6f",
"metadata": {},
"source": [
"data is loaded in long format"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dbd2df5b",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"data.train_X.sample(5)"
]
},
{
"cell_type": "markdown",
"id": "f597ca76",
"metadata": {},
"source": [
"Infer index names from long format"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8e2b780",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"index_columns = list(data.train_X.index.names)\n",
"sample_id = index_columns.pop(args.sample_idx_position)\n",
"if len(index_columns) == 1:\n",
" index_column = index_columns.pop()\n",
" index_columns = None\n",
" logger.info(f\"{sample_id = }, single feature: {index_column = }\")\n",
"else:\n",
" logger.info(f\"{sample_id = }, multiple features: {index_columns = }\")\n",
"\n",
"if not index_columns:\n",
" index_columns = [sample_id, index_column]\n",
"else:\n",
" raise NotImplementedError(\n",
" \"More than one feature: Needs to be implemented. see above logging output.\")"
]
},
{
"cell_type": "markdown",
"id": "29247b80",
"metadata": {},
"source": [
"load meta data for splits"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cd8cc67",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"if args.fn_rawfile_metadata:\n",
" df_meta = pd.read_csv(args.fn_rawfile_metadata, index_col=0)\n",
" display(df_meta.loc[data.train_X.index.levels[0]])\n",
"else:\n",
" df_meta = None"
]
},
{
"cell_type": "markdown",
"id": "23f75036",
"metadata": {},
"source": [
"## Initialize Comparison\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63a9a8c8",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"freq_feat = pimmslearn.io.datasplits.load_freq(args.data)\n",
"freq_feat.head() # training data"
]
},
{
"cell_type": "markdown",
"id": "922d0a8d",
"metadata": {},
"source": [
"### Produce some addional fake samples"
]
},
{
"cell_type": "markdown",
"id": "1ac69d00",
"metadata": {},
"source": [
"The validation simulated NA is used to by all models to evaluate training performance."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5855a725",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"val_pred_fake_na = data.val_y.to_frame(name='observed')\n",
"val_pred_fake_na"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e0ae839",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"test_pred_fake_na = data.test_y.to_frame(name='observed')\n",
"test_pred_fake_na.describe()"
]
},
{
"cell_type": "markdown",
"id": "f4208681",
"metadata": {},
"source": [
"## Data in wide format"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8b41aae",
"metadata": {
"lines_to_next_cell": 2,
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"data.to_wide_format()\n",
"args.M = data.train_X.shape[-1]\n",
"data.train_X.head()"
]
},
{
"cell_type": "markdown",
"id": "c7bf83f1",
"metadata": {},
"source": [
"### Impute using shifted normal distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f5349d6",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"imputed_shifted_normal = pimmslearn.imputation.impute_shifted_normal(\n",
" data.train_X,\n",
" mean_shift=1.8,\n",
" std_shrinkage=0.3,\n",
" completeness=args.completeness,\n",
" axis=args.axis)\n",
"imputed_shifted_normal = imputed_shifted_normal.to_frame('intensity')\n",
"imputed_shifted_normal"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d32d445e",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"val_pred_fake_na[args.model] = imputed_shifted_normal\n",
"test_pred_fake_na[args.model] = imputed_shifted_normal\n",
"val_pred_fake_na"
]
},
{
"cell_type": "markdown",
"id": "72a5da63",
"metadata": {},
"source": [
"Save predictions for NA"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3198a37c",
"metadata": {
"lines_to_next_cell": 0,
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"if args.save_pred_real_na:\n",
" mask = data.train_X.isna().stack()\n",
" idx_real_na = mask.index[mask]\n",
" idx_real_na = (idx_real_na\n",
" .drop(val_pred_fake_na.index)\n",
" .drop(test_pred_fake_na.index))\n",
" # hacky, but works:\n",
" pred_real_na = (pd.Series(0, index=idx_real_na, name='placeholder')\n",
" .to_frame()\n",
" .join(imputed_shifted_normal)\n",
" .drop('placeholder', axis=1))\n",
" # pred_real_na.name = 'intensity'\n",
" display(pred_real_na)\n",
" pred_real_na.to_csv(args.out_preds / f\"pred_real_na_{args.model_key}.csv\")\n",
"\n",
"\n",
"# # %% [markdown]\n",
"# ### Plots\n",
"#"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "df99da67",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"ax, _ = pimmslearn.plotting.errors.plot_errors_binned(val_pred_fake_na)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16637d79",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"ax, _ = pimmslearn.plotting.errors.plot_errors_binned(test_pred_fake_na)"
]
},
{
"cell_type": "markdown",
"id": "3c0f7424",
"metadata": {
"lines_to_next_cell": 2
},
"source": [
"## Comparisons"
]
},
{
"cell_type": "markdown",
"id": "17027ab0",
"metadata": {},
"source": [
"### Validation data\n",
"\n",
"- all measured (identified, observed) peptides in validation data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43d42650",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"# papermill_description=metrics\n",
"d_metrics = models.Metrics()"
]
},
{
"cell_type": "markdown",
"id": "7f5d7459",
"metadata": {},
"source": [
"The fake NA for the validation step are real test data (not used for training nor early stopping)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed0498d0",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"added_metrics = d_metrics.add_metrics(val_pred_fake_na, 'valid_fake_na')\n",
"added_metrics"
]
},
{
"cell_type": "markdown",
"id": "8fa6a5f7",
"metadata": {},
"source": [
"### Test Datasplit\n",
"\n",
"Fake NAs : Artificially created NAs. Some data was sampled and set\n",
"explicitly to misssing before it was fed to the model for\n",
"reconstruction."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ee61d53",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"added_metrics = d_metrics.add_metrics(test_pred_fake_na, 'test_fake_na')\n",
"added_metrics"
]
},
{
"cell_type": "markdown",
"id": "832e3f7c",
"metadata": {},
"source": [
"The fake NA for the validation step are real test data"
]
},
{
"cell_type": "markdown",
"id": "a5a7a319",
"metadata": {},
"source": [
"### Save all metrics as json"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9973b3ee",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"pimmslearn.io.dump_json(d_metrics.metrics, args.out_metrics /\n",
" f'metrics_{args.model_key}.json')\n",
"d_metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b2421c3",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"metrics_df = models.get_df_from_nested_dict(\n",
" d_metrics.metrics, column_levels=['model', 'metric_name']).T\n",
"metrics_df"
]
},
{
"cell_type": "markdown",
"id": "b296a6a1",
"metadata": {},
"source": [
"## Save predictions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39c41bcd",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"# val\n",
"fname = args.out_preds / f\"pred_val_{args.model_key}.csv\"\n",
"setattr(args, fname.stem, fname.as_posix()) # add [] assignment?\n",
"val_pred_fake_na.to_csv(fname)\n",
"# test\n",
"fname = args.out_preds / f\"pred_test_{args.model_key}.csv\"\n",
"setattr(args, fname.stem, fname.as_posix())\n",
"test_pred_fake_na.to_csv(fname)"
]
},
{
"cell_type": "markdown",
"id": "e6a95d00",
"metadata": {},
"source": [
"## Config"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f2f7404",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"figures # switch to fnames?"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8ad37d15",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"args.dump(fname=args.out_models / f\"model_config_{args.model_key}.yaml\")\n",
"args"
]
}
],
"metadata": {
"jupytext": {
"formats": "ipynb,py:percent"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}