Readying initial version

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "TabPFN"]
+	path = TabPFN
+	url = git@github.com:automl/TabPFN.git

README.md

@@ -6,7 +6,7 @@ colorTo: blue
 sdk: gradio
 sdk_version: 3.1.1
 app_file: app.py
-pinned: false
+pinned: true
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

TabPFN

@@ -0,0 +1 @@
+Subproject commit 045c8400203ebd062346970b4f2c0ccda5a40618

app.py

@@ -0,0 +1,95 @@
+import sys
+sys.path.insert(0,'TabPFN') # our submodule of the TabPFN repo
+from scripts.transformer_prediction_interface import TabPFNClassifier
+
+import numpy as np
+import pandas as pd
+import torch
+import gradio as gr
+import openml
+
+
+def compute(table: np.array):
+    vfunc = np.vectorize(lambda s: len(s))
+    non_empty_row_mask = (vfunc(table).sum(1) != 0)
+    table = table[non_empty_row_mask]
+    empty_mask = table == ''
+    empty_inds = np.where(empty_mask)
+    if not len(empty_inds[0]):
+        return "**Please leave at least one field blank for prediction.**", None
+    if not np.all(empty_inds[1][0] == empty_inds[1]):
+        return "**Please only leave fields of one column blank for prediction.**", None
+    y_column = empty_inds[1][0]
+    eval_lines = empty_inds[0]
+
+    train_table = np.delete(table, eval_lines, axis=0)
+    eval_table = table[eval_lines]
+
+    try:
+        x_train = torch.tensor(np.delete(train_table, y_column, axis=1).astype(np.float32))
+        x_eval = torch.tensor(np.delete(eval_table, y_column, axis=1).astype(np.float32))
+
+        y_train = train_table[:, y_column]
+    except ValueError:
+        return "**Please only add numbers (to the inputs) or leave fields empty.**", None
+
+    classifier = TabPFNClassifier(base_path='..', device='cpu')
+    classifier.fit(x_train, y_train)
+    y_eval, p_eval = classifier.predict(x_eval, return_winning_probability=True)
+
+    # print(file, type(file))
+    out_table = table.copy().astype(str)
+    out_table[eval_lines, y_column] = [f"{y_e} (p={p_e:.2f})" for y_e, p_e in zip(y_eval, p_eval)]
+    return None, out_table
+
+
+def upload_file(file):
+    if file.name.endswith('.arff'):
+        dataset = openml.datasets.OpenMLDataset('t', 'test', data_file=file.name)
+        X_, _, categorical_indicator_, attribute_names_ = dataset.get_data(
+            dataset_format="array"
+        )
+        df = pd.DataFrame(X_, columns=attribute_names_)
+        return df
+    elif file.name.endswith('.csv') or file.name.endswith('.data'):
+        df = pd.read_csv(file.name, header=None)
+        df.columns = np.arange(len(df.columns))
+        print(df)
+        return df
+
+
+example = \
+    [
+        [1, 2, 1],
+        [2, 1, 1],
+        [1, 1, 1],
+        [2, 2, 2],
+        [3, 4, 2],
+        [3, 2, 2],
+        [2, 3, '']
+    ]
+
+with gr.Blocks() as demo:
+    gr.Markdown("""This demo allows you to play with the **TabPFN**.
+    You can either change the table manually (we have filled it with a toy benchmark, sum up to 3 has label 1 and over that label 2).
+    The network predicts fields you leave empty. Only one column can have empty entries that are predicted.
+    Please, provide everything but the label column as numeric values. It is ok to encode classes as integers.
+    """)
+    inp_table = gr.DataFrame(type='numpy', value=example, headers=[''] * 3)
+    inp_file = gr.File(
+        label='Drop either a .csv (without header, only numeric values for all but the labels) or a .arff file.')
+    examples = gr.Examples(examples=['iris.csv', 'balance-scale.arff'],
+                           inputs=[inp_file],
+                           outputs=[inp_table],
+                           fn=upload_file,
+                           cache_examples=True)
+    btn = gr.Button("Predict Empty Table Cells")
+
+    inp_file.change(fn=upload_file, inputs=inp_file, outputs=inp_table)
+
+    out_text = gr.Markdown()
+    out_table = gr.DataFrame()
+
+    btn.click(fn=compute, inputs=inp_table, outputs=[out_text, out_table])
+
+demo.launch()

balance-scale.arff

@@ -0,0 +1,694 @@
+%1. Title: Balance Scale Weight & Distance Database
+%
+%2. Source Information: 
+%    (a) Source: Generated to model psychological experiments reported
+%		by Siegler, R. S. (1976).  Three Aspects of Cognitive
+%		Development.  Cognitive Psychology, 8, 481-520.
+%    (b) Donor: Tim Hume (hume@ics.uci.edu)
+%    (c) Date: 22 April 1994
+%
+%3. Past Usage: (possibly different formats of this data)
+%   - Publications
+%	1. Klahr, D., & Siegler, R.S. (1978).  The Representation of
+%	   Children's Knowledge.  In H. W. Reese & L. P. Lipsitt (Eds.),
+%	   Advances in Child Development and Behavior, pp. 61-116.  New
+%	   York: Academic Press 
+%	2. Langley,P. (1987).  A General Theory of Discrimination
+%	   Learning.  In D. Klahr, P. Langley, & R. Neches (Eds.),
+%	   Production System Models of Learning and Development, pp.
+%	   99-161. Cambridge, MA: MIT Press
+%	3. Newell, A. (1990).  Unified Theories of Cognition.
+%	   Cambridge, MA: Harvard University Press
+%	4. McClelland, J.L. (1988).  Parallel Distibuted Processing:
+%	   Implications for Cognition and Development.  Technical
+%	   Report AIP-47, Department of Psychology, Carnegie-Mellon
+%	   University 
+%	5. Shultz, T., Mareschal, D., & Schmidt, W. (1994).  Modeling
+%	   Cognitive Development on Balance Scale Phenomena. Machine
+%	   Learning, Vol. 16, pp. 59-88.
+%
+%4. Relevant Information: 
+%	This data set was generated to model psychological
+%	experimental results.  Each example is classified as having the
+%	balance scale tip to the right, tip to the left, or be
+%	balanced.  The attributes are the left weight, the left
+%	distance, the right weight, and the right distance.  The
+%	correct way to find the class is the greater of 
+%	(left-distance * left-weight) and (right-distance *
+%	right-weight).  If they are equal, it is balanced.
+%
+%5. Number of Instances: 625 (49 balanced, 288 left, 288 right)
+%
+%6. Number of Attributes: 4 (numeric) + class name = 5
+%
+%7. Attribute Information:
+%	1. Class Name: 3 (L, B, R)
+%	2. Left-Weight: 5 (1, 2, 3, 4, 5)
+%	3. Left-Distance: 5 (1, 2, 3, 4, 5)
+%	4. Right-Weight: 5 (1, 2, 3, 4, 5)
+%	5. Right-Distance: 5 (1, 2, 3, 4, 5)
+%
+%8. Missing Attribute Values: 
+%	none
+%
+%9. Class Distribution: 
+%   1. 46.08 percent are L
+%   2. 07.84 percent are B
+%   3. 46.08 percent are R
+%
+
+@relation balance-scale
+@attribute 'left-weight' real
+@attribute 'left-distance' real
+@attribute 'right-weight' real
+@attribute 'right-distance' real
+@attribute 'class' { L, B, R}
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+4,4,4,5,R
+4,4,5,1,L
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+4,4,5,5,R
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+5,1,1,1,L
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+5,1,4,1,L
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+5,3,3,5,B
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+%
+%
+%

iris.csv

@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+

requirements.txt

@@ -0,0 +1,16 @@
+# Please use python V 3.7 to be compatible with all packages
+gpytorch==1.5.0
+torch==1.9.0
+scikit-learn==0.24.2
+pyyaml==5.4.1
+seaborn==0.11.2
+xgboost==1.4.0
+tqdm==4.62.1
+numpy==1.21.2
+openml==0.12.2
+catboost==0.26.1
+auto-sklearn==0.14.5
+hyperopt==0.2.5
+configspace==0.4.21
+# autogluon==0.4.0
+gradio==3.1.1