schrodinger.active_learning.al_report module

schrodinger.active_learning.al_report.get_ligand_ml_metric(ligand_ml_model_file)

Extract the test set metrics, test set labels and predictions from ligand_ml model file.

Parameters

ligand_ml_model_file (str) – ligand_ml .qzip model file.

Returns

r2, mae, rmse, labels and prediction of the test set

Return type

float, float, float, 1d numpy array, 2d numpy array

schrodinger.active_learning.al_report.make_train_report(ligand_ml_model_file, report_path, iter_num)

Generate a pdf file that records the test set metrics of the ligand_ml model.

Parameters

  • ligand_ml_model_file (str) – ligand_ml .qzip model file.

  • report_path (str) – path of the pdf report

  • iter_num (int) – current iteration number

schrodinger.active_learning.al_report.make_score_hist_plot(name_to_scores, x_label, plot_path)

Generate a bar-type histogram plot which contains multiple data. The names of the data are used as the legends.

Parameters

  • name_to_scores (dict(str:list)) – a dictionary where keys are used as the legends and values are the numbers to be plotted.

  • x_label (str) – label of x-axis

  • plot_path (str) – path of the saved plot

schrodinger.active_learning.al_report.get_image(path, width=72.0)

Convert image file to reportlab image object that has the same aspect ratio and specified width.

Parameters

  • path (str) – path of the image file.

  • width (float) – width of the reportlab image.

Returns

reportlab image

Return type

reportlab.platypus.Image

schrodinger.active_learning.al_report.get_report_maker(active_learning_job)

Get corresponding report maker for the active learning job. It returns None for evaluate task since we do not have report for it yet.

Parameters

active_learning_job (ActiveLearningJob) – active learning job to be processed.

Returns

corresponding report maker

Return type

ALPilotReportMaker

schrodinger.active_learning.al_report.get_time_cost(nodes, node_name)

Return the time cost of a node. It returns ‘Unavailable’ if the time cost is not available.

Parameters

  • nodes (dict{str: ActiveLearningNode}) – dict that maps node name to node object.

  • node_name (str) – name of the active learning node of interest.

Returns

time cost in h/m/s format.

Return type

str

schrodinger.active_learning.al_report.get_score_pred_as_array(title_to_score, pred_score_file, discard_cutoff, ascending=True)

Return the score, predicted score, prediction uncertainty of the ligands as the N X 3 numpy array.

Parameters

  • title_to_score (dict(str:float)) – dict that maps ligand title to score.

  • pred_score_file (str) – path of the ligand ml prediction .csv file.

  • discard_cutoff (float) – score cutoff for excluding the ligands in ML training set.

  • ascending (bool) – lower value means better ligand if ascending is True

Returns

numpy array of (num_of_ligands X (score, pred, uncertain))

Return type

N X 3 numpy array

schrodinger.active_learning.al_report.calculate_recovery_ratio(label_pred, top_ratio)

Calculate the recovery ratio of the best ligands based on label in different numbers of the top ligands predicted by ligand_ml. More negative value means better ligand.

Parameters

  • label_pred ((number of ligands X 2) numpy array.) – numpy array contains the (label, prediction).

  • top_ratio (float) – top ratio of the ligands by label.

Returns

(screen ratio, recovery ratio of top ligands defined by top_ratio) of all the ligands.

Return type

(number of ligands X 2) numpy array

schrodinger.active_learning.al_report.plot_regression(y_true, y_pred, fname)

Generate regression plot. This function is sightly modified from ligand_ml/plotting.py to change the labels of axis.

Parameters

  • y_true (1d numpy array) – test set label.

  • y_pred (2d numpy array) – ligand_ml prediction and uncertainty

  • fname (str) – filename to save the image

schrodinger.active_learning.al_report.plot_recovery(recovery_results, fname)

Generate and save recovery plot image.

Parameters

  • recovery_results (dict{float:np.array}) – dict that maps top ratio to the recovery ratio numpy array.

  • fname (str) – path of the saved image.

schrodinger.active_learning.al_report.make_regress_recovery_plots(y_true, y_pred_uncertain, top_ratio_samples, regress_text, recovery_text)

Generate regression plot and recovery plot and include both in a table. Also return the recovery results for the sampled top ratios as a dict.

schrodinger.active_learning.al_report.make_recovery_table(recovery_results, screen_ratio_samples)

Generate a list of list that contains the recovery ratio for certain top ratio and screen ratio.

Parameters

  • recovery_results (dict{float:np.array}) – dict that maps top ratio to the recovery ratio numpy array.

  • screen_ratio_samples (list(float)) – list of screen ratios

Returns

table as a list of list, table caption, largest enrichment in the table.

Return type

list(list(str)), str, float

schrodinger.active_learning.al_report.get_conclusion_string(best_enrichment, job_type, high_enrich=10, low_enrich=2)

Return the conclusion string based on the job type and the higheest enrichment we have in the recovery ratio table.

class schrodinger.active_learning.al_report.ALReportMaker(active_learning_job)

Bases: object

Base class for different types of AL report maker.

__init__(active_learning_job)

Initialize the report maker for an active learning job

initReport(header)

Initialize the report and add header information

class schrodinger.active_learning.al_report.ALPilotReportMaker(active_learning_job)

Bases: schrodinger.active_learning.al_report.ALReportMaker

__init__(active_learning_job)

Initialize the report maker for an active learning job

report()

Function for building the report

addRunDetail()

Add job specifications and running time cost information to the report

addRecoveryResults()

Add the regression plot, recovery plot, recovery table and conclusion to the report.

initReport(header)

Initialize the report and add header information

class schrodinger.active_learning.al_report.ALScreenReportMaker(active_learning_job)

Bases: schrodinger.active_learning.al_report.ALReportMaker

__init__(active_learning_job)

Initialize the report maker for an active learning job

report()

Function for building the report

addRunDetail()

Add job specifications and running time cost information to the report

addRecoveryResults()

Add the regression plot, recovery plot, recovery table and conclusion to the report.

initReport(header)

Initialize the report and add header information