sparks-baird / mat_discover Goto Github PK

InvalidParameterError                     Traceback (most recent call last)
Cell In[16], line 13
     11 for i, val_df in enumerate(val_dfs):
     12     jd = jd + 1
---> 13     disc.predict(val_df, umap_random_state=42)
     14     dens_score.append(disc.dens_score_df.sample(frac=1))
     15     peak_score.append(disc.peak_score_df.sample(frac=1))

File c:\Users\hasan\miniconda3\envs\li_ml\lib\site-packages\mat_discover\mat_discover_.py:665, in Discover.predict(self, val_df, plotting, umap_random_state, pred_weight, proxy_weight, dummy_run, count_repeats, return_peak)
    663 # compound-wise scores (i.e. individual compounds)
    664 with self.Timer("nearest-neighbor-properties"):
--> 665     self.rad_neigh_avg_targ, self.k_neigh_avg_targ = nearest_neigh_props(
    666         self.dm, pred, n_neighbors=self.n_peak_neighbors
    667     )
    668     self.val_rad_neigh_avg = self.rad_neigh_avg_targ[val_ids]
    669     self.val_k_neigh_avg = self.k_neigh_avg_targ[val_ids]

File c:\Users\hasan\miniconda3\envs\li_ml\lib\site-packages\mat_discover\utils\nearest_neigh.py:48, in nearest_neigh_props(X, target, r_strength, radius, n_neighbors, metric, **NN_kwargs)
      6 def nearest_neigh_props(
      7     X,
      8     target,
   (...)
     13     **NN_kwargs,
     14 ):
...
     98     f"The {param_name!r} parameter of {caller_name} must be"
     99     f" {constraints_str}. Got {param_val!r} instead."
    100 )

InvalidParameterError: The 'radius' parameter of NearestNeighbors must be a float in the range [0, inf] or None. Got -0.28528690338134766 instead.

val_dfs = np.array_split(val_df, 3)

print("##########chunking done##########")

# %% directories
table_dirs = [join("tables", val) for val in ["val1", "val2", "val3"]]
figure_dirs = [join("figures", val) for val in ["val1", "val2", "val3"]]
# %% fit, predict, plot
disc.fit(train_df)

print("##########fitting done##########")

for i, val_df in enumerate(val_dfs):
    # change output dirs
    disc.table_dir = table_dirs[i]
    disc.figure_dir = figure_dirs[i]

    # %% predict
    score = disc.predict(val_df, umap_random_state=42)

    print("##########predictions done##########")

    # %% plot and save
    disc.plot()

FileNotFoundError                         Traceback (most recent call last)
<ipython-input-32-7f8d2c805a2e> in <module>()
     16 
     17     # %% plot and save
---> 18     disc.plot()
     19     disc.save(dummy=dummy)
     20 

7 frames
/usr/local/lib/python3.7/dist-packages/mat_discover/mat_discover_.py in plot(self, return_pareto_ind)
   1318             Pareto front indices for the peak and density proxies, respectively.
   1319         """
-> 1320         fig, pk_pareto_ind = self.pf_peak_proxy()
   1321         fig, frac_pareto_ind = self.pf_train_contrib_proxy()
   1322 

/usr/local/lib/python3.7/dist-packages/mat_discover/mat_discover_.py in pf_peak_proxy(self)
   1544             x_unit=self.target_unit,
   1545             y_unit=self.target_unit,
-> 1546             use_plotly_offline=self.use_plotly_offline,
   1547         )
   1548 

/usr/local/lib/python3.7/dist-packages/mat_discover/utils/pareto.py in pareto_plot(df, x, y, color, x_unit, y_unit, color_unit, hover_data, fpath, reverse_x, parity_type, pareto_front, color_continuous_scale, color_discrete_map, xrange, use_plotly_offline)
    214 
    215     if fpath is not None:
--> 216         fig.write_html(fpath + ".html")
    217 
    218     fig, scale = matplotlibify(fig)

/usr/local/lib/python3.7/dist-packages/plotly/basedatatypes.py in write_html(self, *args, **kwargs)
   3706         import plotly.io as pio
   3707 
-> 3708         return pio.write_html(self, *args, **kwargs)
   3709 
   3710     def to_image(self, *args, **kwargs):

/usr/local/lib/python3.7/dist-packages/plotly/io/_html.py in write_html(fig, file, config, auto_play, include_plotlyjs, include_mathjax, post_script, full_html, animation_opts, validate, default_width, default_height, auto_open, div_id)
    534     # Write HTML string
    535     if path is not None:
--> 536         path.write_text(html_str)
    537     else:
    538         file.write(html_str)

/usr/lib/python3.7/pathlib.py in write_text(self, data, encoding, errors)
   1238             raise TypeError('data must be str, not %s' %
   1239                             data.__class__.__name__)
-> 1240         with self.open(mode='w', encoding=encoding, errors=errors) as f:
   1241             return f.write(data)
   1242 

/usr/lib/python3.7/pathlib.py in open(self, mode, buffering, encoding, errors, newline)
   1206             self._raise_closed()
   1207         return io.open(self, mode, buffering, encoding, errors, newline,
-> 1208                        opener=self._opener)
   1209 
   1210     def read_bytes(self):

/usr/lib/python3.7/pathlib.py in _opener(self, name, flags, mode)
   1061     def _opener(self, name, flags, mode=0o666):
   1062         # A stub for the opener argument to built-in open()
-> 1063         return self._accessor.open(self, flags, mode)
   1064 
   1065     def _raw_open(self, flags, mode=0o777):

FileNotFoundError: [Errno 2] No such file or directory: 'figures/val1/pf-peak-proxy.html

conda update conda
conda create --name mat_discover python==3.9.*
conda activate mat_discover
conda install -c sgbaird mat_discover

Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: |
Found conflicts! Looking for incompatible packages.
This can take several minutes.  Press CTRL-C to abort.
failed

UnsatisfiableError: The following specifications were found to be incompatible with each other:

Output in format: Requested package -> Available versions

>> disc=Discover()
>>> disc.load(fpath='C:/Users/anton/OneDrive/Desktop/matdisc_colab/formula_discharge vs capacity_grav/disc.pkl')
<mat_discover.mat_discover_.Discover object at 0x0000015006F56D90>

>>> disc.cluster_avg()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'Discover' object has no attribute 'cluster_avg'

>>> disc.merge()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\anton\miniconda3\envs\matdisc\lib\site-packages\mat_discover\mat_discover_.py", line 848, in merge
    dens_score_df = self.dens_score_df.rename(columns={"score": "Density Score"})
AttributeError: 'Discover' object has no attribute 'dens_score_df'

>>> disc.compute_log_density()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\anton\miniconda3\envs\matdisc\lib\site-packages\mat_discover\mat_discover_.py", line 1261, in compute_log_density
    r_orig = self.std_r_orig
AttributeError: 'Discover' object has no attribute 'std_r_orig'

>>> from mat_discover.mat_discover_ import Discover   
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\anton\anaconda3\envs\mat-disc\lib\site-packages\mat_discover\mat_discover_.py", line 36, in <module>
    import umap
  File "C:\Users\anton\anaconda3\envs\mat-disc\lib\site-packages\umap\__init__.py", line 2, in <module>
    from .umap_ import UMAP
  File "C:\Users\anton\anaconda3\envs\mat-disc\lib\site-packages\umap\umap_.py", line 28, in <module>
    import numba
  File "C:\Users\anton\anaconda3\envs\mat-disc\lib\site-packages\numba\__init__.py", line 200, in <module>
    _ensure_critical_deps()
  File "C:\Users\anton\anaconda3\envs\mat-disc\lib\site-packages\numba\__init__.py", line 140, in _ensure_critical_deps
    raise ImportError("Numba needs NumPy 1.21 or less")
ImportError: Numba needs NumPy 1.21 or less

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
matminer 0.7.6 requires numpy>=1.22.0, but you have numpy 1.21.0 which is incompatible.