Spaces:
Running
Running
Preprocessing in main script
Browse files- app.py +28 -5
- create_index.py +0 -67
app.py
CHANGED
@@ -1,8 +1,11 @@
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import os
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import crystal_toolkit.components as ctc
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import dash
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import dash_mp_components as dmp
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from crystal_toolkit.settings import SETTINGS
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from dash import dcc, html
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from dash.dependencies import Input, Output, State
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@@ -56,13 +59,33 @@ display_names = {
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mapping_table_idx_dataset_idx = {}
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import numpy as np
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import periodictable
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map_periodic_table = {v.symbol: k for k, v in enumerate(periodictable.elements)}
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dataset_index = np.
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# Initialize the Dash app
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app = dash.Dash(__name__, assets_folder=SETTINGS.ASSETS_PATH)
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import os
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import re
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import crystal_toolkit.components as ctc
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import dash
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import dash_mp_components as dmp
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import numpy as np
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import periodictable
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from crystal_toolkit.settings import SETTINGS
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from dash import dcc, html
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from dash.dependencies import Input, Output, State
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mapping_table_idx_dataset_idx = {}
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map_periodic_table = {v.symbol: k for k, v in enumerate(periodictable.elements)}
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dataset_index = np.zeros((len(dataset), 118))
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train_df = dataset.to_pandas()
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pattern = re.compile(r"(?P<element>[A-Z][a-z]?)(?P<count>\d*)")
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extracted = train_df["chemical_formula_descriptive"].str.extractall(pattern)
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extracted["count"] = extracted["count"].replace("", "1").astype(int)
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wide_df = extracted.reset_index().pivot_table( # Move index to columns for pivoting
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index="level_0", # original row index
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columns="element",
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values="count",
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aggfunc="sum",
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fill_value=0,
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)
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all_elements = [el.symbol for el in periodictable.elements] # full element list
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wide_df = wide_df.reindex(columns=all_elements, fill_value=0)
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dataset_index = wide_df.values
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dataset_index = dataset_index / np.sum(dataset_index, axis=1)[:, None]
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dataset_index = (
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dataset_index / np.linalg.norm(dataset_index, axis=1)[:, None]
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) # Normalize vectors
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# Initialize the Dash app
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app = dash.Dash(__name__, assets_folder=SETTINGS.ASSETS_PATH)
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create_index.py
DELETED
@@ -1,67 +0,0 @@
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import os
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import re
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import numpy as np
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import periodictable
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from datasets import load_dataset
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HF_TOKEN = os.environ.get("HF_TOKEN")
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# Load only the train split of the dataset
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dataset = load_dataset(
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"LeMaterial/leDataset",
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token=HF_TOKEN,
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split="train",
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columns=[
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"lattice_vectors",
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"species_at_sites",
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"cartesian_site_positions",
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"energy",
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"energy_corrected",
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"immutable_id",
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"elements",
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"functional",
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"stress_tensor",
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"magnetic_moments",
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"forces",
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"band_gap_direct",
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"band_gap_indirect",
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"dos_ef",
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"charges",
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"functional",
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"chemical_formula_reduced",
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"chemical_formula_descriptive",
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"total_magnetization",
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],
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)
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map_periodic_table = {v.symbol: k for k, v in enumerate(periodictable.elements)}
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dataset_index = np.zeros((len(dataset), 118))
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train_df = dataset.to_pandas()
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pattern = re.compile(r"(?P<element>[A-Z][a-z]?)(?P<count>\d*)")
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extracted = train_df["chemical_formula_descriptive"].str.extractall(pattern)
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extracted["count"] = extracted["count"].replace("", "1").astype(int)
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wide_df = extracted.reset_index().pivot_table( # Move index to columns for pivoting
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index="level_0", # original row index
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columns="element",
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values="count",
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aggfunc="sum",
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fill_value=0,
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)
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all_elements = [el.symbol for el in periodictable.elements] # full element list
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wide_df = wide_df.reindex(columns=all_elements, fill_value=0)
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dataset_index = wide_df.values
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dataset_index = dataset_index / np.sum(dataset_index, axis=1)[:, None]
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dataset_index = (
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dataset_index / np.linalg.norm(dataset_index, axis=1)[:, None]
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) # Normalize vectors
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np.save("dataset_index.npy", dataset_index)
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