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+tmpp_deeplc_ready,3.0758751926634686,5.195896515657522,4.776101478475579,44.2402,91.2279,46.987700000000004,1245.0,5.235351771196727,5.6955125741768935,3.3716387121302454 +tmpp_deeplc_ready_24bands,1.825805930466483,3.0158236850160063,0.5837173786559758,17.3516,36.4883,19.1367,4054.0,1.59973848783302,8.265180030354953,5.003811990326989 diff --git a/deeplc/deeplc.py b/deeplc/deeplc.py index 66a5610..4eb50d8 100644 --- a/deeplc/deeplc.py +++ b/deeplc/deeplc.py @@ -890,12 +890,12 @@ def calibrate_preds(self, infile="", measured_tr=[], correction_factor=1.0, - location_peprec_retraining="", location_retraining_models="", psm_utils_obj=None, sample_for_calibration_curve=None, seq_df=None, - use_median=True): + use_median=True, + return_plotly_report=True): """ Find best model and calibrate. @@ -929,7 +929,8 @@ def calibrate_preds(self, for seq,mod,ident,tr in zip(seq_df["seq"],seq_df["modifications"],seq_df.index,seq_df["tr"]): list_of_psms.append(PSM(peptidoform=peprec_to_proforma(seq,mod),spectrum_id=ident,retention_time=tr)) psm_list = PSMList(psm_list=list_of_psms) - + else: + psm_list = psm_utils_obj if isinstance(self.model, str): self.model = [self.model] @@ -959,6 +960,8 @@ def calibrate_preds(self, mod_calibrate_max_dict = {} pred_dict = {} mod_dict = {} + temp_obs = [] + temp_pred = [] if self.deeplc_retrain: # The following code is not required in most cases, but here it is used to clear variables that might cause problems @@ -985,7 +988,7 @@ def calibrate_preds(self, outpath=t_dir_models, mods_transfer_learning=self.model, freeze_layers=True, - n_epochs=10, + n_epochs=20, freeze_after_concat=1, ) @@ -1044,7 +1047,7 @@ def calibrate_preds(self, mod_calibrate_dict[m_group_name][m] = self.calibrate_dict mod_calibrate_min_dict[m_group_name][m] = self.calibrate_min mod_calibrate_max_dict[m_group_name][m] = self.calibrate_max - except: + except KeyError: pred_dict[m_group_name] = {} mod_dict[m_group_name] = {} mod_calibrate_dict[m_group_name] = {} @@ -1084,6 +1087,9 @@ def calibrate_preds(self, best_model = copy.deepcopy(mod_dict[m_group_name]) best_perf = perf + temp_obs = np.array(measured_tr) + temp_pred = np.array(preds) + self.calibrate_dict = best_calibrate_dict self.calibrate_min = best_calibrate_min self.calibrate_max = best_calibrate_max @@ -1096,6 +1102,75 @@ def calibrate_preds(self, logger.debug("Model with the best performance got selected: %s" % (best_model)) + if return_plotly_report: + plotly_return_dict = {} + plotly_df = pd.DataFrame( + list(zip(temp_obs,temp_pred)), + columns=["Observed retention time (min)","Predicted retention time (min)"] + ) + + plotly_return_dict["scatter"] = self._make_plotly_report_scatter(plotly_df) + plotly_return_dict["baseline_dist"] = self._make_plotly_report_dist_baseline(plotly_df) + return plotly_return_dict + + return {} + + def _make_plotly_report_scatter(self,df): + import plotly.express as px + fig = px.scatter( + df, + x="Observed retention time (min)", + y="Predicted retention time (min)" + ) + + fig.update_layout(shapes = [{'type': 'line', 'yref': 'paper', 'xref': 'paper', + 'y0': min(df["Observed retention time (min)"]), + 'y1': max(df["Observed retention time (min)"]), + 'x0': min(df["Observed retention time (min)"]), + 'x1': max(df["Observed retention time (min)"])}]) + + return fig + + def _make_plotly_report_dist_baseline(self,df): + import plotly.figure_factory as ff + + baseline_df = pd.read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), "baseline_performance/baseline_predictions.csv")) + baseline_df.fillna(0.0,inplace=True) + + mae = sum(abs(df["Observed retention time (min)"]-df["Predicted retention time (min)"]))/len(df.index) + mae_rel = (mae/max(df["Observed retention time (min)"]))*100 + + #rel_mae_transfer_learning rel_mae_new_model rel_mae_calibrate + group_labels = ['Baseline performance'] # name of the dataset + + + + fig = ff.create_distplot( + [list(baseline_df["rel_mae_transfer_learning"])], + group_labels, + rug_text=list(baseline_df.index), + ) + + fig.update_xaxes(title_text="Relative Mean Absolute Error") + #fig.update_yaxes(title_text="Density") + + fig.update_layout(title=f"Current DeepLC performance compared to {len(baseline_df.index)} datasets") + + percentile = round((baseline_df["rel_mae_transfer_learning"] < mae_rel).mean()*100,1) + fig.add_scatter(x=[mae_rel], + y=['distplot'], + mode='markers', + marker=dict(color='red', symbol='x'), + name=f"Current (percentile: {percentile}%)", + xaxis= 'x', + yaxis='y2') + + #improve the plot aesthetics: + fig.update_traces(selector=dict(type="histogram"), marker_line_color="black", marker_line_width=1.5) + #fig.update_layout(width=700, height=500) + + return fig + def split_seq(self, a, n): """ Split a list (a) into multiple chunks (n)