Update the documentation after adding the GUI and configuration file.

Please note, until further notice, only the 'rescue_version' branch is functional. All other branches are out of service

add tooltips for options "local" and "server" to avoid looking at the help to know what will change in the way to handle the program

Rewrite the basic of cLASpy_T (common.py, training.py, predict.py) using classes to have a more comprehensive code and easier to maintain.

enlever "about" car il est déjà en haut à gauche pour plus de lisibilité, corriger les addresses email de laurent et christophe

Many breacking changes between laspy 1.7.0 and 2.2.0. Need to update cLASpy_T.
Change dtype of extra_diemnsions from float64 to float32

First run with the "run" button. Did not get any results. Will try again with different examples.

C:\Users\laurent\AppData\Local\Programs\Python\Python38\python.exe G:/WORK/machine_learning/code_xavier_150720/cLASpy_T/cLASpy_GUI.py

Process finished with exit code -1073740791 (0xC0000409)

[laurent@hulk cLASpy_T-Formation-CNRS]$ source .venv/claspy_venv/bin/activate
(claspy_venv) [laurent@hulk cLASpy_T-Formation-CNRS]$ python cLASpy_T.py train --train_r=0.1 -a=gb -i=/home/laurent/machine/xavier_orne/130525_1Mpts.las -f=['Anisotropy_(5)','Anisotropy_(10)','Anisotropy_(25)','Anisotropy_(50)','Eigenentropy_(5)','Eigenentropy_(10)','Eigenentropy_(25)','Eigenentropy_(50)','Eigenvalues_sum_(5)','Eigenvalues_sum_(10)','Eigenvalues_sum_(25)','Eigenvalues_sum_(50)','linearity_(5)','linearity_(10)','linearity_(25)','linearity_(50)','Omnivariance_(5)','Omnivariance_(10)','Omnivariance_(25)','Omnivariance_(50)','PCA1_(5)','PCA1_(10)','PCA1_(25)','PCA1_(50)','PCA2_(5)','PCA2_(10)','PCA2_(25)','PCA2_(50)','Planarity_(5)','Planarity_(10)','Planarity_(25)','Planarity_(50)','Roughness_(5)','Roughness_(10)','Roughness_(25)','Roughness_(50)','Sphericity_(5)','Sphericity_(10)','Sphericity_(25)','Sphericity_(50)','Surface_variation_(5)','Surface_variation_(10)','Surface_variation_(25)','Surface_variation_(50)','Verticality_(5)','Verticality_(10)','Verticality_(25)','Verticality_(50)','Verticality_(5)','Verticality_(10)','Verticality_(25)','Verticality_(50)'] -p="{'n_estimators':100,'max_depth':20,'min_samples_leaf':100}"

####### POINT CLOUD CLASSIFICATION #######
Algorithm used: GradientBoostingClassifier
Path to LAS file: /home/laurent/machine/xavier_orne/130525_1Mpts.las

Create a new folder to store the result files... Folder already exists.

Step 1/7: Formatting data as pandas.Dataframe...
LAS Version: 1.2
LAS point format: 1
Number of points: 1,000,000
/home/laurent/machine/cLASpy_T-Formation-CNRS/common.py:276: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling frame.insert many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use newframe = frame.copy()
frame[dim] = las.get_reader().get_dimension(dim)

Get selected features:

• Anisotropy_(5) asked --> Anisotropy_(5) found
• Anisotropy_(10) asked --> Anisotropy_(10) found
• Anisotropy_(25) asked --> Anisotropy_(25) found
• Anisotropy_(50) asked --> Anisotropy_(50) found
• Eigenentropy_(5) asked --> Eigenentropy_(5) found
• Eigenentropy_(10) asked --> Eigenentropy_(10) found
• Eigenentropy_(25) asked --> Eigenentropy_(25) found
• Eigenentropy_(50) asked --> Eigenentropy_(50) found
• Eigenvalues_sum_(5) asked --> Eigenvalues_sum_(5) found
• Eigenvalues_sum_(10) asked --> Eigenvalues_sum_(10) found
• Eigenvalues_sum_(25) asked --> Eigenvalues_sum_(25) found
• Eigenvalues_sum_(50) asked --> Eigenvalues_sum_(50) found
• linearity_(5) asked --> Linearity_(5) found
• linearity_(10) asked --> Linearity_(10) found
• linearity_(25) asked --> Linearity_(25) found
• linearity_(50) asked --> Linearity_(50) found
• Omnivariance_(5) asked --> Omnivariance_(5) found
• Omnivariance_(10) asked --> Omnivariance_(10) found
• Omnivariance_(25) asked --> Omnivariance_(25) found
• Omnivariance_(50) asked --> Omnivariance_(50) found
• PCA1_(5) asked --> PCA1_(5) found
• PCA1_(10) asked --> PCA1_(10) found
• PCA1_(25) asked --> PCA1_(25) found
• PCA1_(50) asked --> PCA1_(50) found
• PCA2_(5) asked --> PCA2_(5) found
• PCA2_(10) asked --> PCA2_(10) found
• PCA2_(25) asked --> PCA2_(25) found
• PCA2_(50) asked --> PCA2_(50) found
• Planarity_(5) asked --> Planarity_(5) found
• Planarity_(10) asked --> Planarity_(10) found
• Planarity_(25) asked --> Planarity_(25) found
• Planarity_(50) asked --> Planarity_(50) found
• Roughness_(5) asked --> Roughness_(5) found
• Roughness_(10) asked --> Roughness_(10) found
• Roughness_(25) asked --> Roughness_(25) found
• Roughness_(50) asked --> Roughness_(50) found
• Sphericity_(5) asked --> Sphericity_(5) found
• Sphericity_(10) asked --> Sphericity_(10) found
• Sphericity_(25) asked --> Sphericity_(25) found
• Sphericity_(50) asked --> Sphericity_(50) found
• Surface_variation_(5) asked --> Surface_variation_(5) found
• Surface_variation_(10) asked --> Surface_variation_(10) found
• Surface_variation_(25) asked --> Surface_variation_(25) found
• Surface_variation_(50) asked --> Surface_variation_(50) found
• Verticality_(5) asked --> Verticality_(5) found
• Verticality_(10) asked --> Verticality_(10) found
• Verticality_(25) asked --> Verticality_(25) found
• Verticality_(50) asked --> Verticality_(50) found
• Verticality_(5) asked --> Verticality_(5) found
• Verticality_(10) asked --> Verticality_(10) found
• Verticality_(25) asked --> Verticality_(25) found
• Verticality_(50) asked --> Verticality_(50) found

Number of wanted features: 52
Number of final selected features: 52

--> All required features are present!

/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/pandas/core/generic.py:6392: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
return self._update_inplace(result)
/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/pandas/core/frame.py:5177: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
downcast=downcast,

Step 2/7: Splitting data in train and test sets...
Random_state to split data: 0
Number of used points: 1 000 000 pts
Size of train|test datasets: 100 000 pts | 900 000 pts

Step 3/7: Scaling data...

Step 4/7: Training model with cross validation...

Random_state for the StratifiedShuffleSplit: 0
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 24 concurrent workers.
/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/ensemble/_gb.py", line 413, in fit
dtype=DTYPE, multi_output=True)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/base.py", line 433, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 878, in check_X_y
estimator=estimator)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 721, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 106, in _assert_all_finite
msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

FitFailedWarning)
[CV] END .................................................... total time= 0.2s
/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/ensemble/_gb.py", line 413, in fit
dtype=DTYPE, multi_output=True)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/base.py", line 433, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 878, in check_X_y
estimator=estimator)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 721, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 106, in _assert_all_finite
msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

FitFailedWarning)
[CV] END .................................................... total time= 0.2s
/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/ensemble/_gb.py", line 413, in fit
dtype=DTYPE, multi_output=True)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/base.py", line 433, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 878, in check_X_y
estimator=estimator)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 721, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 106, in _assert_all_finite
msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

FitFailedWarning)
[CV] END .................................................... total time= 0.2s
[Parallel(n_jobs=-1)]: Done 3 out of 5 | elapsed: 2.1s remaining: 1.4s
/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/ensemble/_gb.py", line 413, in fit
dtype=DTYPE, multi_output=True)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/base.py", line 433, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 878, in check_X_y
estimator=estimator)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 721, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 106, in _assert_all_finite
msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

FitFailedWarning)
[CV] END .................................................... total time= 0.2s
/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py:619: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details:
Traceback (most recent call last):
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/model_selection/_validation.py", line 598, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/pipeline.py", line 346, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/ensemble/_gb.py", line 413, in fit
dtype=DTYPE, multi_output=True)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/base.py", line 433, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 878, in check_X_y
estimator=estimator)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 721, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 106, in _assert_all_finite
msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

FitFailedWarning)
[CV] END .................................................... total time= 0.2s
[Parallel(n_jobs=-1)]: Done 5 out of 5 | elapsed: 2.2s finished

    Training model scores with cross-validation:
    [nan nan nan nan nan]

Model trained!

Step 5/7: Creating confusion matrix...
Traceback (most recent call last):
File "cLASpy_T.py", line 300, in
args.func(args)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/training.py", line 520, in train
y_test_pred = model.predict(x_test)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/metaestimators.py", line 120, in
out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/pipeline.py", line 419, in predict
return self.steps[-1][-1].predict(Xt, **predict_params)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/ensemble/_gb.py", line 1188, in predict
raw_predictions = self.decision_function(X)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/ensemble/_gb.py", line 1143, in decision_function
X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 721, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/home/laurent/machine/cLASpy_T-Formation-CNRS/.venv/claspy_venv/lib64/python3.7/site-packages/sklearn/utils/validation.py", line 106, in _assert_all_finite
msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
(claspy_venv) [laurent@hulk cLASpy_T-Formation-CNRS]$

Make the GUI adaptable for any screen resolution and format, by user.

add a logo
suggestion: a snake (python) navigation through a point cloud !

Addition of a fail safe to avoid errors from feature field repetition in the --features option.

Program stops at step " 2. Splitting data in train and test sets..."
"
Traceback (most recent call last):
File "cLASpy_T.py", line 212, in
samples=nbr_pts)
File "G:\WORK\machine_learning\code_xavier_150720\cLASpy_T\training.py", line 71, in split_dataset
target_train = target_train.reshape(train_size)
ValueError: cannot reshape array of size 458 into shape (432468,)
"

(claspy_venv) D:\Ayoub\cLaspy_T>python cLASpy_T.py predict -i "D:\Ayoub\4classe_nuage_de_points\MACHINE_Learning_4classes\211103_10Mpts_target_4classes.las" -m "D:\Ayoub\4classe_nuage_de_points\MACHINE_Learning_4classes\211103_300kpts_target_4classes\train_rf300kpts_0322_1339.model" -o "D:\Ayoub\4classe_nuage_de_points\MACHINE_Learning_4classes\nuage"

# # # # # # # # # cLASpy_T # # # # # # # # # # #

• • • • • • • • PREDICT MODE - - - - - - - - - -

• • • • Point Cloud Classification * * * * * *

Step 1/6: Loading model...

    Any PCA data to load from model.

Algorithm used: RandomForestClassifier
Path to LAS file: D:\Ayoub\4classe_nuage_de_points\MACHINE_Learning_4classes\211103_10Mpts_target_4classes.las

Create a new folder to store the result files... Folder already exists.

LAS Version: 1.4
LAS point format: 7
Number of points: 10,000,000

Step 2/6: Formatting data as pandas.DataFrame...

Get selected features: - B asked --> B found

• G asked --> G found
• Planarity_(0.05) asked --> Planarity_(0.05) found
• Planarity_(0.5) asked --> Planarity_(0.5) found
• Planarity_(1) asked --> Planarity_(1) found
• R asked --> R found
• Sphericity_(0.05) asked --> Sphericity_(0.05) found
• Sphericity_(0.5) asked --> Sphericity_(0.5) found
• Sphericity_(1) asked --> Sphericity_(1) found
• Verticality_(0.05) asked --> Verticality_(0.05) found
• Verticality_(0.5) asked --> Verticality_(0.5) found
• Verticality_(1) asked --> Verticality_(1) found
• Volume_density_(0.05) asked --> Volume_density_(0.05) found
• Volume_density_(0.5) asked --> Volume_density_(0.5) found
• Volume_density_(1) asked --> Volume_density_(1) found

Number of wanted features: 15
Number of final selected features: 15
--> All required features are present!

Step 3/6: Scaling data...

Scale dataset with Scaler transform

Step 4/6: Making predictions for entire dataset...

Target field find -> Create confusion matrix

          precision    recall  f1-score   support

     0.0       0.95      0.89      0.92   2284104
     1.0       0.91      0.96      0.94   3450827
     2.0       0.95      0.97      0.96   2793397
     3.0       0.93      0.87      0.90   1471672

accuracy                           0.93  10000000

macro avg 0.94 0.92 0.93 10000000
weighted avg 0.93 0.93 0.93 10000000

Step 5/6: Saving classified point cloud:
Traceback (most recent call last):
File "cLASpy_T.py", line 592, in
predict(args)
File "cLASpy_T.py", line 526, in predict
step5 = predicter.save_predictions(verbose=True)
File "D:\Ayoub\cLaspy_T\cLASpy_Classes.py", line 1142, in save_predictions
output_las = laspy.file.File(self.report_filename + '.las', mode="w", header=las.header)
File "D:\Ayoub\cLaspy_T.venv\claspy_venv\lib\site-packages\laspy\file.py", line 64, in init
self.open()
File "D:\Ayoub\cLaspy_T.venv\claspy_venv\lib\site-packages\laspy\file.py", line 121, in open
self._writer = base.Writer(self.filename, mode = "w",
File "D:\Ayoub\cLaspy_T.venv\claspy_venv\lib\site-packages\laspy\base.py", line 338, in init
self.setup_write(header, vlrs, evlrs)
File "D:\Ayoub\cLaspy_T.venv\claspy_venv\lib\site-packages\laspy\base.py", line 435, in setup_write
self.populate_evlrs()
File "D:\Ayoub\cLaspy_T.venv\claspy_venv\lib\site-packages\laspy\base.py", line 607, in populate_evlrs
self.seek(self.header.start_first_evlr, rel = False)
File "D:\Ayoub\cLaspy_T.venv\claspy_venv\lib\site-packages\laspy\base.py", line 529, in seek
self.data_provider._mmap.seek(bytes, 0)
ValueError: seek out of range

--n_jobs option does not respect the number of CPU chosen by the user.
Raised by Laurent124

OSError occurs when loading configuration file for training in cLASpy_GUI, with reference to a non-existing file or directory (input file)

Add support of config file (.txt or .json...)

laspy 2.0.2 in requirement.txt while laspy 1.7.0 is needed.

add license information
add software logo
can we add institution logo ? rsg, m2c, cnrs ?

Bug lorsque l'on coche "ne plus afficher la fenêtre"

Lors du prochain lancement, le logiciel plante à la ligne 312:
self.pythonPath = self.options_dict['python_path']

avec le message suivant dans le terminal

C:\Users\laurent\AppData\Local\Programs\Python\Python38\python.exe "C:\Program Files\JetBrains\PyCharm Community Edition 2019.2.1\helpers\pydev\pydevd.py" --multiproc --qt-support=auto --client 127.0.0.1 --port 63501 --file E:/Formation_CNRS/Contenu/cLASpy_T-dev/cLASpy_T-dev/cLASpy_GUI.py
pydev debugger: process 9860 is connecting

Connected to pydev debugger (build 192.7142.79)
Traceback (most recent call last):
File "E:/Formation_CNRS/Contenu/cLASpy_T-dev/cLASpy_T-dev/cLASpy_GUI.py", line 312, in init
self.pythonPath = self.options_dict['python_path']
KeyError: 'python_path'
Backend Qt5Agg is interactive backend. Turning interactive mode on.

Use LAS files as input of cLASpy-T instead of CSV files, with le LASpy library.

Rewrite README.md according the changes made by the claspy_gui branch.

When loading model created after a GridSearchCV, the cLASpy_GUI crashes

Launching a training with claspy_t with a config file took about 48h whereas the same run would last 4h30 using direct commands. I m attaching the config files (one with plot and one without) as well as the direct command line. I renamed the .json files into .txt files as github forbids uploading .json files.

[P2_cas23.txt](https://github.co
run23_command.txt
m/TrickyPells/cLASpy_T/files/8024732/P2_cas23.txt)
P2_cas23_no_plot.txt

Adapt 'run cLASpy_T' methods to run it on Linux.

Write the instructions of how to use cLASpy_T.

Give estimation of time to process train or prediction (example: subscaling large data set by 100)

Step 1/7: Formatting data as pandas.Dataframe...
LAS Version: 1.2
LAS point format: 2
Number of points: 820,731
All features in input_data will be used!
Traceback (most recent call last):
File "cLASpy_T.py", line 300, in
args.func(args)
File "C:\cLASpy_T\training.py", line 432, in train
data, target = format_dataset(data_path, mode=mode, features=args.features)
File "C:\cLASpy_T\common.py", line 372, in format_dataset
selected_features.remove(field)
ValueError: list.remove(x): x not in list

while using RF with Gridsearch in tab_modes, n_jobs RF makes the program crash while using n_jobs_CV only works

Add HistGradientBoostingClassifier to cLASpy_T.
HGB can be orders of magnitude faster than Grandient Boosting for point clouds > 10 000 samples (i.e. all 3D point clouds).

Add all required packages in requirements file to install all dependencies automatically with pip.

Add in the documentation that the "intensity" field needs to be duplicate and renamed to be used

Make the button 'run cLASpy_T' usefull, for real !

GUI - Create a prediction mode and separate the 2 modes: "Training" and "Prediction"

Add Tab to launch several runs of cLASpy_T.

For Prediction, if one or more features are missing, the Warning Box could list all missing features.

Target not present in selected features

Add option to set the training/testing ratio in the cLASpy_GUI.py

Make the progress bar functional when the process runs.

the save_prediction function does not work with the 'segment' subcommand.
"cLASpy_T\predict.py", line 172, in save_predictions
if predictions.shape[1] > 2:
IndexError: tuple index out of range

In 'claspy_gui' branch

would avoid lots of directory navigation

save_feature_importance() function try to plot Principal Component but only find the feature names. So it raises KeyError with the feature name of the number_components + 1.
"Traceback (most recent call last):
File "cLASpy_T.py", line 301, in

args.func(args)

File "D:\Code_Source\Repositories\cLASpy_T\training.py", line 517, in train
save_feature_importance(model, feature_names, feat_imp_filename)
File "D:\Code_Source\Repositories\cLASpy_T\common.py", line 651, in save_feature_importance
importances_sorted.append(feature_imp_dict[key])
KeyError: 'B'"

When press the push button 'Train', 'Predict' or 'Segment', nothing appears in the Command Output box, but CPU and RAM is used. When the job is done, all the output command is prompted in the command output box.