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private_inverse_design_alloy

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snajmark commited on Jul 2, 2023

Commit

a3639a3

1 Parent(s): 6fd74af

Update app.py

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Files changed (1) hide show

app.py +66 -8

app.py CHANGED Viewed

@@ -72,7 +72,15 @@ def write_logs(message, message_type="Prediction"):
     #             )
     print(message)
     return
 def predict(x, request: gr.Request):
     """
     Predict the hardness and yield strength using the ML model. Input data is a dataframe
@@ -139,7 +147,60 @@ def predict_from_tuple(in1, in2, in3, in4, in5, request: gr.Request):
 def predict_inverse(target_hardness, target_yield_strength, request: gr.Request):
-    return "Al1", "S", "BCC", "CAST", "HCP"
 example_inputs = [420, 10]
@@ -170,7 +231,7 @@ html = f"""<html> <link rel="icon" type="image/x-icon" href="file={favicon_path}
 <img src='file={logo_path}' alt='Osium AI logo' width='200' height='100'> </html>"""
-with gr.Blocks(css=css_styling, title=page_title) as demo:
     gr.HTML(html)
     gr.Markdown("# <p style='text-align: center;'>Get optimal alloy recommendations based on your target performance</p>")
     gr.Markdown("This AI model provides a recommended alloy formula, microstructure and processing conditions based on your target hardness and yield strength")
@@ -203,6 +264,7 @@ with gr.Blocks(css=css_styling, title=page_title) as demo:
                 choices=unique_phase_elements, #list(input_mapping["PROPERTY: Microstructure"].keys()),
                 label=input_cols["PROPERTY: Microstructure"],
             )
         # with gr.Column():
         #     gr.Markdown("### Your alloy's yield strength (MPa)")
@@ -218,11 +280,7 @@ with gr.Blocks(css=css_styling, title=page_title) as demo:
         fn=predict_inverse,
         inputs=[input_hardness, input_yield_strength],
         outputs=[
-            output_formula,
-            output_phase,
-            output_bccfcc,
-            output_processing,
-            output_microstructure
         ],
         show_progress=True,
     )

     #             )
     print(message)
     return
+def fit_outputs(x, hardness_target, ys_target):
+    predictions = predict(x)[0]
+    error_hardness = np.sqrt(np.square(predictions[0]-hardness_target))
+    error_ys = np.sqrt(np.square(predictions[1]-ys_target))
+    print(predictions, hardness_target, ys_target, error_hardness, error_ys)
+    return error_hardness + error_ys
 def predict(x, request: gr.Request):
     """
     Predict the hardness and yield strength using the ML model. Input data is a dataframe
 def predict_inverse(target_hardness, target_yield_strength, request: gr.Request):
+    continuous_variables = ['PROPERTY: Calculated Density (g/cm$^3$)',
+                         'PROPERTY: Calculated Young modulus (GPa)']
+    categorical_variables = list(one_hot.columns)
+    for c in continuous_variables:
+        categorical_variables.remove(c)
+    domain = []
+    for c in one_hot.columns:
+        if c in continuous_variables:
+            if c == continuous_variables[0]:
+                domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.528, 0.528)})#(0.,1.)})
+            else:
+                domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.539, 0.539)})#(0.,1.)})
+        else:
+            domain.append({'name': str(c), 'type': 'discrete', 'domain': (0,1)})
+    constraints = []
+    constrained_columns = ['Single/Multiphase', 'Preprocessing method', 'BCC/FCC/other']#, 'Microstructure']
+    for constraint in constrained_columns:
+        sum_string = ''
+        for i in range (len(one_hot.columns)):
+            column_one_hot = one_hot.columns[i]
+            if column_one_hot.startswith(constraint):
+                sum_string = sum_string+"+x[:," + str(i) + "]"
+        constraints.append({'name': constraint + "+1", 'constraint': sum_string + '-1'})
+        constraints.append({'name': constraint + "-1", 'constraint': '-1*(' + sum_string + ')+1'})
+    opt = GPyOpt.methods.BayesianOptimization(f = fit_outputs,            # function to optimize
+                                              domain = domain,        # box-constraints of the problem
+                                              constraints = constraints,
+                                              acquisition_type ='LCB',       # LCB acquisition
+                                              acquisition_weight = 0.1)   # Exploration exploitation
+    # it may take a few seconds
+    opt.run_optimization(max_iter=50)
+    opt.plot_convergence()
+    x_best = opt.X[np.argmin(opt.Y)]
+    best_params = dict(zip(
+        [el['name'] for el in domain],
+        [[x] for x in x_best]))
+    optimized_x = pd.DataFrame.from_dict(best_params)
+    for c in optimized_x.columns:
+        if c in continuous_variables:
+            optimized_x[c]=optimized_x[c]*(dictionary[c][1]-dictionary[c][0])+dictionary[c][0]
+    optimized_x = optimized_x[['PROPERTY: Calculated Density (g/cm$^3$)',
+                               'PROPERTY: Calculated Young modulus (GPa)',
+                               'Preprocessing method ANNEAL',
+                               'Preprocessing method CAST', 'Preprocessing method OTHER',
+                               'Preprocessing method POWDER', 'Preprocessing method WROUGHT',
+                               'BCC/FCC/other BCC', 'BCC/FCC/other FCC', 'BCC/FCC/other OTHER',
+                               'Single/Multiphase ', 'Single/Multiphase M', 'Single/Multiphase S']]
+    result = optimized_x
+    result = result[result>0.0].dropna(axis=1)
+    return result
 example_inputs = [420, 10]
 <img src='file={logo_path}' alt='Osium AI logo' width='200' height='100'> </html>"""
+with gr.Blocks(css=css_styling, title=page_title, theme=osium_theme) as demo:
     gr.HTML(html)
     gr.Markdown("# <p style='text-align: center;'>Get optimal alloy recommendations based on your target performance</p>")
     gr.Markdown("This AI model provides a recommended alloy formula, microstructure and processing conditions based on your target hardness and yield strength")
                 choices=unique_phase_elements, #list(input_mapping["PROPERTY: Microstructure"].keys()),
                 label=input_cols["PROPERTY: Microstructure"],
             )
+            optimal_parameters = gr.DataFrame(label="Optimal parameters")
         # with gr.Column():
         #     gr.Markdown("### Your alloy's yield strength (MPa)")
         fn=predict_inverse,
         inputs=[input_hardness, input_yield_strength],
         outputs=[
+            optimal_parameters
         ],
         show_progress=True,
     )